Note: This project involves getting data ready for analysis and doing some preliminary investigations. Project 2 will involve modeling and predictions on the same dataset, and will be released at a later date. Both projects will have equal weightage towards your grade. You may reuse some of the preprocessing/analysis steps from Project 1 in Project 2.

Data

In this project, you will explore a dataset that contains information about movies, including ratings, budget, gross revenue and other attributes. It was prepared by Dr. Guy Lebanon, and here is his description of the dataset:

The file movies_merged contains a dataframe with the same name that has 40K rows and 39 columns. Each row represents a movie title and each column represents a descriptor such as Title, Actors, and Budget. I collected the data by querying IMDb’s API (see www.omdbapi.com) and joining it with a separate dataset of movie budgets and gross earnings (unknown to you). The join key was the movie title. This data is available for personal use, but IMDb’s terms of service do not allow it to be used for commercial purposes or for creating a competing repository.

Objective

Your goal is to investigate the relationship between the movie descriptors and the box office success of movies, as represented by the variable Gross. This task is extremely important as it can help a studio decide which titles to fund for production, how much to bid on produced movies, when to release a title, how much to invest in marketing and PR, etc. This information is most useful before a title is released, but it is still very valuable after the movie is already released to the public (for example it can affect additional marketing spend or how much a studio should negotiate with on-demand streaming companies for “second window” streaming rights).

Instructions

This is an R Markdown Notebook. Open this file in RStudio to get started.

When you execute code within the notebook, the results appear beneath the code. Try executing this chunk by clicking the Run button within the chunk or by placing your cursor inside it and pressing Cmd+Shift+Enter.

# Project 1: http://cse6242.gatech.edu/fall-2017/pr1/
# To compile R Markdown in terminal run: Rscript -e "rmarkdown::render('pr1.Rmd', clean=TRUE)"
x = 1:10
print(x^2)
 [1]   1   4   9  16  25  36  49  64  81 100

Plots appear inline too:

plot(x, x^2, 'o')

Add a new chunk by clicking the Insert Chunk button on the toolbar or by pressing Cmd+Option+I. Enter some R code and run it.

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the Preview button or press Cmd+Shift+K to preview the HTML file).

Please complete all the tasks below by implementing code chunks that have a TODO comment in them, running all code chunks so that output and plots are displayed, and typing in answers to each question (Q: …) next to/below the corresponding answer prompt (A:). Feel free to add code chunks/show additional output to support any of the answers.

When you are done, you will need to submit the final R markdown file (as pr1.Rmd) with all code chunks implemented and executed, and all text responses written in. You also need to submit a PDF export of the markdown file (as pr1.pdf), which should show your code, output, plots and written responses–this will be your project report. Compress these two files into a single .zip archive and upload it on T-Square.

Setup

Load data

Make sure you’ve downloaded the movies_merged file and it is in the current working directory. Now load it into memory:

load('movies_merged')
cat("Dataset has", dim(movies_merged)[1], "rows and", dim(movies_merged)[2], "columns", end="\n", file="")
Dataset has 40789 rows and 39 columns 

This creates an object of the same name (movies_merged). For convenience, you can copy it to df and start using it:

df = movies_merged
cat("Column names:", end="\n", file="")
Column names: 
colnames(df)
 [1] "Title"             "Year"              "Rated"            
 [4] "Released"          "Runtime"           "Genre"            
 [7] "Director"          "Writer"            "Actors"           
[10] "Plot"              "Language"          "Country"          
[13] "Awards"            "Poster"            "Metascore"        
[16] "imdbRating"        "imdbVotes"         "imdbID"           
[19] "Type"              "tomatoMeter"       "tomatoImage"      
[22] "tomatoRating"      "tomatoReviews"     "tomatoFresh"      
[25] "tomatoRotten"      "tomatoConsensus"   "tomatoUserMeter"  
[28] "tomatoUserRating"  "tomatoUserReviews" "tomatoURL"        
[31] "DVD"               "BoxOffice"         "Production"       
[34] "Website"           "Response"          "Budget"           
[37] "Domestic_Gross"    "Gross"             "Date"             
Title

Year

Rated

Released

Runtime

Genre

Director

Writer

Actors

Plot

Language

Country

Awards

Poster

Metascore

imdbRating

imdbVotes

imdbID

Type

tomatoMeter

tomatoImage

tomatoRating

tomatoReviews

tomatoFresh

tomatoRotten

tomatoConsensus

tomatoUserMeter

tomatoUserRating

tomatoUserReviews

tomatoURL

DVD

BoxOffice

Production

Website

Response

Budget

Domestic_Gross

Gross

Date

Load R packages

Load any R packages that you will need to use. You can come back to this chunk, edit it and re-run to load any additional packages later.

library(ggplot2)
library(GGally)

# Note that I'm using Anaconda R, and I was having trouble installing qdapTools. I'm not sure
# if this is the same on other machines but I did have to install R XML separately
# https://anaconda.org/r/r-xml
# conda install -c r r-xml. Only after could I install qdapTools.
library(qdapTools)

If you are loading any non-standard packages (ones that have not been discussed in class or explicitly allowed for this project), please mention them below. Include any special instructions if they cannot be installed using the regular install.packages('<pkg name>') command.

Non-standard packages used: None

Tasks

Each task below is worth 10 points, and is meant to be performed sequentially, i.e. do step 2 after you have processed the data as described in step 1. Total points: 100

Complete each task by implementing code chunks as described by TODO comments, and by responding to questions (“Q:”) with written answers (“A:”). If you are unable to find a meaningful or strong relationship in any of the cases when requested, explain why not by referring to appropriate plots/statistics.

It is okay to handle missing values below by omission, but please omit as little as possible. It is worthwhile to invest in reusable and clear code as you may need to use it or modify it in project 2.

1. Remove non-movie rows

The variable Type captures whether the row is a movie, a TV series, or a game. Remove all rows from df that do not correspond to movies.

# TODO: Remove all rows from df that do not correspond to movies
df2 <- df[df$Type == "movie",]
original_dim = dim(df)
new_dim = dim(df2)

df = df2
# Differences in rows
print(original_dim[1] - new_dim[1])
[1] 789

Q: How many rows are left after removal? Enter your response below.

A: The number of rows left after removal are 40000.

2. Process Runtime column

The variable Runtime represents the length of the title as a string. Write R code to convert it to a numeric value (in minutes) and replace df$Runtime with the new numeric column.

extract_runtime = function(r){
    times = unlist(r)
    minutes = 0
    for (i in 1:length(times) - 1){
        if (times[i + 1] == 'h'){
            minutes = minutes + as.numeric(times[i]) * 60
        } else if (times[i + 1] == 'min'){
            minutes = minutes + as.numeric(times[i])
        }
    }
    if (minutes == 0){
        return(NA)
    } else{
        return(minutes)
    }
}

y=strsplit(df$Runtime,' ')
new_runtimes = unlist(lapply(y, extract_runtime))
df$Runtime = new_runtimes

cols = c('Runtime', 'Year', 'Budget')
summary(df[cols])
    Runtime            Year          Budget         
 Min.   :  1.00   Min.   :1888   Min.   :     1100  
 1st Qu.: 72.00   1st Qu.:1961   1st Qu.:  5000000  
 Median : 90.00   Median :1989   Median : 18000000  
 Mean   : 81.79   Mean   :1981   Mean   : 31661108  
 3rd Qu.:101.00   3rd Qu.:2001   3rd Qu.: 40000000  
 Max.   :873.00   Max.   :2018   Max.   :425000000  
 NA's   :751                     NA's   :35442      

Now investigate the distribution of Runtime values and how it changes over years (variable Year, which you can bucket into decades) and in relation to the budget (variable Budget). Include any plots that illustrate.

# TODO: Investigate the distribution of Runtime values and how it varies by Year and Budget
ggplot(df, aes(x=Runtime)) +
  geom_histogram() +
  ggtitle('Histogram of Runtime (minutes)')
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Warning: Removed 751 rows containing non-finite values (stat_bin).

ggplot(df, aes(Year, Runtime)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")
Warning: Removed 751 rows containing non-finite values (stat_summary).

ggplot(df, aes(Budget, Runtime)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")
Warning: Removed 35480 rows containing non-finite values (stat_summary).

Feel free to insert additional code chunks as necessary.

Q: Comment on the distribution as well as relationships. Are there any patterns or trends that you can observe?

A:

Looking in the aggregate, it seems like most runtimes are under 2 hours. There is a clear relationship between run time and year, looking a bit like a sigmoid function. This makes sense as very early years, it was probably technologically prohibitive to create long movies. As technology improved, runtimes increased, but there’s only so much time you can expect to hold an audience captive. Interestingly enough, during the 1990’s there seems to be a downward trend in runtimes until around mid 2000’s where it picked up again.

3. Encode Genre column

The column Genre represents a list of genres associated with the movie in a string format. Write code to parse each text string into a binary vector with 1s representing the presence of a genre and 0s the absence, and add it to the dataframe as additional columns. Then remove the original Genre column.

For example, if there are a total of 3 genres: Drama, Comedy, and Action, a movie that is both Action and Comedy should be represented by a binary vector <0, 1, 1>. Note that you need to first compile a dictionary of all possible genres and then figure out which movie has which genres (you can use the R tm package to create the dictionary).

# TODO: Replace Genre with a collection of binary columns
require(tm)
Loading required package: tm
Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
logical.return = TRUE, : there is no package called 'tm'
# All unique genres in the dataframe
print(unique(unlist(strsplit(df$Genre, ', '))))
 [1] "Documentary" "Biography"   "Romance"     "Short"       "Thriller"   
 [6] "Drama"       "War"         "Comedy"      "Horror"      "Sci-Fi"     
[11] "Adventure"   "Family"      "History"     "Crime"       "Action"     
[16] "Music"       "Mystery"     "Fantasy"     "Sport"       "Animation"  
[21] "Musical"     "N/A"         "Talk-Show"   "Adult"       "Western"    
[26] "Film-Noir"   "Reality-TV"  "News"        "Game-Show"  
# Example of how to one-hot encode: https://stackoverflow.com/questions/39778387/r-dataframe-one-hot-encoding-of-column-containing-multiple-terms

df = cbind(df, mtabulate(strsplit(df$Genre, ", ")))

cols = c('Genre', 'Action', 'Adult', 'Adventure', 'Animation', 'Biography', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Family', 'Fantasy', 'Film-Noir', 'Game-Show', 'History', 'Horror', 'Music', 'Musical', 'Mystery', 'N/A', 'News', 'Reality-TV', 'Romance', 'Sci-Fi', 'Short', 'Sport', 'Talk-Show', 'Thriller', 'War', 'Western')

# Remove column from dataframe
df$Genre = NULL

Plot the relative proportions of movies having the top 10 most common genres.

# TODO: Select movies from top 10 most common genres and plot their relative proportions

# See https://stackoverflow.com/questions/20679702/r-find-column-with-the-largest-column-sum as reference
cols = setdiff(cols, 'Genre')
colCount = colSums(df[cols])
topTenIds = order(colCount,decreasing=TRUE)[1:10] + 1
topTenCols = names(df[cols][topTenIds])

# The top 10 most common genres are
print(topTenCols)
 [1] "Family"      "Crime"       "Sport"       "Sci-Fi"      "Adult"      
 [6] "Documentary" "War"         "Drama"       "Animation"   "Biography"  
# Probably need to do some manual "stacking" of dataframes by each of the top 10 most common genres.
# Note that I was somewhat confused on what "relative proportions of movies" meant. I found this helpful
# https://piazza.com/class/j6gt7ycx6nk145?cid=396
# Suppose you have ten ice creams.  Let's say that four of them are Chocolate ice cream.  What is the relative proportion of Chocolate ice cream ?   --> 4 / 10
cols3 = c('Title', 'Runtime', topTenCols)
df3 = df[cols3]
print(summary(df3))
    Title              Runtime           Family            Crime       
 Length:40000       Min.   :  1.00   Min.   :0.00000   Min.   :0.0000  
 Class :character   1st Qu.: 72.00   1st Qu.:0.00000   1st Qu.:0.0000  
 Mode  :character   Median : 90.00   Median :0.00000   Median :0.0000  
                    Mean   : 81.79   Mean   :0.06632   Mean   :0.1016  
                    3rd Qu.:101.00   3rd Qu.:0.00000   3rd Qu.:0.0000  
                    Max.   :873.00   Max.   :1.00000   Max.   :1.0000  
                    NA's   :751                                        
     Sport             Sci-Fi           Adult          Documentary     
 Min.   :0.00000   Min.   :0.0000   Min.   :0.00000   Min.   :0.00000  
 1st Qu.:0.00000   1st Qu.:0.0000   1st Qu.:0.00000   1st Qu.:0.00000  
 Median :0.00000   Median :0.0000   Median :0.00000   Median :0.00000  
 Mean   :0.01312   Mean   :0.0406   Mean   :0.01055   Mean   :0.07627  
 3rd Qu.:0.00000   3rd Qu.:0.0000   3rd Qu.:0.00000   3rd Qu.:0.00000  
 Max.   :1.00000   Max.   :1.0000   Max.   :1.00000   Max.   :1.00000  
                                                                       
      War              Drama          Animation        Biography      
 Min.   :0.00000   Min.   :0.0000   Min.   :0.0000   Min.   :0.00000  
 1st Qu.:0.00000   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.00000  
 Median :0.00000   Median :0.0000   Median :0.0000   Median :0.00000  
 Mean   :0.02675   Mean   :0.3965   Mean   :0.0697   Mean   :0.02772  
 3rd Qu.:0.00000   3rd Qu.:1.0000   3rd Qu.:0.0000   3rd Qu.:0.00000  
 Max.   :1.00000   Max.   :1.0000   Max.   :1.0000   Max.   :1.00000  
                                                                      
print(head(df3))
              Title Runtime Family Crime Sport Sci-Fi Adult Documentary
1 39 Pounds of Love      70      0     0     0      0     0           1
2              3:am      21      0     0     0      0     0           0
3   500 Years Later     106      0     0     0      0     0           1
4         5th World      75      0     0     0      0     0           0
5                90      14      0     0     0      0     0           0
6  Abel Raises Cain      82      0     0     0      0     0           1
  War Drama Animation Biography
1   0     0         0         1
2   0     0         0         0
3   0     0         0         0
4   0     1         0         0
5   1     0         0         0
6   0     0         0         1
library(reshape2)
df3.long = melt(df3, id.vars=c('Title', 'Runtime'))

# Removing rows where value is 0 to answer the question
# This is because when value is 0, that means movie is not that genre.
df3.long = df3.long[apply(df3.long['value'],1,function(z) !any(z==0)),]
print(summary(df3.long))
    Title              Runtime              variable         value  
 Length:33163       Min.   :  1.00   Drama      :15859   Min.   :1  
 Class :character   1st Qu.: 73.00   Crime      : 4062   1st Qu.:1  
 Mode  :character   Median : 91.00   Documentary: 3051   Median :1  
                    Mean   : 83.88   Animation  : 2788   Mean   :1  
                    3rd Qu.:105.00   Family     : 2653   3rd Qu.:1  
                    Max.   :873.00   Sci-Fi     : 1624   Max.   :1  
                    NA's   :407      (Other)    : 3126              
print(head(df3.long))
                               Title Runtime variable value
11                Aliens of the Deep     100   Family     1
21                 Are We There Yet?      95   Family     1
29             Because of Winn-Dixie     106   Family     1
64                    Down and Derby      90   Family     1
68                              Duma     100   Family     1
73 Eliana em O Segredo dos Golfinhos      94   Family     1
# Finally plot relative proportions
# https://sebastiansauer.github.io/percentage_plot_ggplot2_V2/
ggplot(df3.long, aes(x=variable)) +
  geom_bar(aes(y = (..count..)/sum(..count..))) +
  ylab('Relative Proportion')

Examine how the distribution of Runtime changes across genres for the top 10 most common genres.

# TODO: Plot Runtime distribution for top 10 most common genres
# ggplot(df3.long, aes(x=Runtime)) +
#   geom_density(aes(color=variable, group=variable)) +
#   ggtitle('Distribution of Runtimes by Movie Genre')

ggplot(df3.long, aes(as.factor(variable), Runtime)) +
  geom_boxplot() +
  coord_flip() +
  scale_x_discrete("Genre")
Warning: Removed 407 rows containing non-finite values (stat_boxplot).

Q: Describe the interesting relationship(s) you observe. Are there any expected or unexpected trends that are evident?

A: One thing that immediately stood out to me was that in looking at the extreme outliers (Runtimes greater than 300 minutes for example or 5 hours), most of these very long movies tend to be Drama, War, and Documentaries, which fits anecdotally with my priors of what movies would be extremely long.

Animation films tend to be the shortest, which is expected as there are probably many short animation films. I was actually surprised to see that most animation films are very very short.

One that that was unexpected to me was that with the exception of Animation and Family films, most of the median runtimes for all the rest of the genres are very similar.

4. Eliminate mismatched rows

The dataframe was put together by merging two different sources of data and it is possible that the merging process was inaccurate in some cases (the merge was done based on movie title, but there are cases of different movies with the same title). There are 3 columns that contain date information: Year (numeric year), Date (numeric year), and Released (string representation of the release date).

Find and remove all rows where you suspect a merge error occurred based on a mismatch between these variables. To make sure subsequent analysis and modeling work well, avoid removing more than 10% of the rows that have a Gross value present.

Note: Do not remove the rows with Gross == NA at this point, just use this a guideline.

# TODO: Remove rows with Year/Date/Released mismatch
library(lubridate)  # Used to extract year from Released
Loading required package: methods

Attaching package: 'lubridate'
The following object is masked from 'package:base':

    date
cols4 = c('Year', 'Date', 'Released', 'Gross')
df4 = df[cols4]
df4$released_year = year(df4$Released)
df4$not_matched = (df4$Year != df4$released_year) & (df4$Year != df4$Date) & (df4$Date != df4$released_year)

# Number of rows with non-null Gross Value
print(nrow(df[which(!is.na(df$Gross)), ]))
[1] 4558
# Printing rows in the original data set
print(nrow(df))
[1] 40000
df = df[which(df4$not_matched == FALSE | is.na(df4$not_matched)), ]
print(nrow(df))
[1] 39952

Q: What is your precise removal logic, and how many rows remain in the resulting dataset?

A: Out of the 40,000 original rows, I removed rows where Year != released_year and Year != Date and Date != released_year This resulted in 48 rows to be removed where there was a non-null Gross value. Since there were originally 4510 rows with a non-null Gross value, we have indeed removed less than 10 percent of the rows with non-null Gross values.

There are now 39952 rows in the resulting dataset.

5. Explore Gross revenue

For the commercial success of a movie, production houses want to maximize Gross revenue. Investigate if Gross revenue is related to Budget, Runtime or Genre in any way.

Note: To get a meaningful relationship, you may have to partition the movies into subsets such as short vs. long duration, or by genre, etc.

# TODO: Investigate if Gross Revenue is related to Budget, Runtime or Genre
cols = c('Title', 'Gross', 'Runtime', 'Budget', 'Released', 'Action', 'Adult', 'Adventure', 'Animation', 'Biography', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Family', 'Fantasy', 'Film-Noir', 'Game-Show', 'History', 'Horror', 'Music', 'Musical', 'Mystery', 'N/A', 'News', 'Reality-TV', 'Romance', 'Sci-Fi', 'Short', 'Sport', 'Talk-Show', 'Thriller', 'War', 'Western')
df5 = df[cols]

print(summary(df5))
    Title               Gross              Runtime      
 Length:39952       Min.   :0.000e+00   Min.   :  1.00  
 Class :character   1st Qu.:5.000e+06   1st Qu.: 72.00  
 Mode  :character   Median :3.006e+07   Median : 90.00  
                    Mean   :9.057e+07   Mean   : 81.78  
                    3rd Qu.:1.008e+08   3rd Qu.:101.00  
                    Max.   :2.784e+09   Max.   :873.00  
                    NA's   :35442       NA's   :751     
     Budget             Released              Action      
 Min.   :     1100   Min.   :1893-05-09   Min.   :0.0000  
 1st Qu.:  5000000   1st Qu.:1959-02-04   1st Qu.:0.0000  
 Median : 18000000   Median :1989-10-06   Median :0.0000  
 Mean   : 31952913   Mean   :1980-12-02   Mean   :0.1104  
 3rd Qu.: 40000000   3rd Qu.:2001-11-07   3rd Qu.:0.0000  
 Max.   :425000000   Max.   :2018-08-01   Max.   :1.0000  
 NA's   :35442       NA's   :4949                         
     Adult           Adventure         Animation         Biography      
 Min.   :0.00000   Min.   :0.00000   Min.   :0.00000   Min.   :0.00000  
 1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.00000  
 Median :0.00000   Median :0.00000   Median :0.00000   Median :0.00000  
 Mean   :0.01056   Mean   :0.07324   Mean   :0.06976   Mean   :0.02768  
 3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.00000  
 Max.   :1.00000   Max.   :1.00000   Max.   :1.00000   Max.   :1.00000  
                                                                        
     Comedy           Crime         Documentary          Drama       
 Min.   :0.0000   Min.   :0.0000   Min.   :0.00000   Min.   :0.0000  
 1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.00000   1st Qu.:0.0000  
 Median :0.0000   Median :0.0000   Median :0.00000   Median :0.0000  
 Mean   :0.3212   Mean   :0.1014   Mean   :0.07624   Mean   :0.3963  
 3rd Qu.:1.0000   3rd Qu.:0.0000   3rd Qu.:0.00000   3rd Qu.:1.0000  
 Max.   :1.0000   Max.   :1.0000   Max.   :1.00000   Max.   :1.0000  
                                                                     
     Family           Fantasy          Film-Noir          Game-Show       
 Min.   :0.00000   Min.   :0.00000   Min.   :0.000000   Min.   :0.00e+00  
 1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.000000   1st Qu.:0.00e+00  
 Median :0.00000   Median :0.00000   Median :0.000000   Median :0.00e+00  
 Mean   :0.06638   Mean   :0.03502   Mean   :0.008811   Mean   :5.01e-05  
 3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.000000   3rd Qu.:0.00e+00  
 Max.   :1.00000   Max.   :1.00000   Max.   :1.000000   Max.   :1.00e+00  
                                                                          
    History            Horror            Music            Musical       
 Min.   :0.00000   Min.   :0.00000   Min.   :0.00000   Min.   :0.00000  
 1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.00000  
 Median :0.00000   Median :0.00000   Median :0.00000   Median :0.00000  
 Mean   :0.02075   Mean   :0.06781   Mean   :0.02961   Mean   :0.03469  
 3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.00000  
 Max.   :1.00000   Max.   :1.00000   Max.   :1.00000   Max.   :1.00000  
                                                                        
    Mystery             N/A               News          
 Min.   :0.00000   Min.   :0.00000   Min.   :0.0000000  
 1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.0000000  
 Median :0.00000   Median :0.00000   Median :0.0000000  
 Mean   :0.04097   Mean   :0.02468   Mean   :0.0005006  
 3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.0000000  
 Max.   :1.00000   Max.   :1.00000   Max.   :1.0000000  
                                                        
   Reality-TV           Romance           Sci-Fi            Short       
 Min.   :0.0000000   Min.   :0.0000   Min.   :0.00000   Min.   :0.0000  
 1st Qu.:0.0000000   1st Qu.:0.0000   1st Qu.:0.00000   1st Qu.:0.0000  
 Median :0.0000000   Median :0.0000   Median :0.00000   Median :0.0000  
 Mean   :0.0001502   Mean   :0.1243   Mean   :0.04052   Mean   :0.1631  
 3rd Qu.:0.0000000   3rd Qu.:0.0000   3rd Qu.:0.00000   3rd Qu.:0.0000  
 Max.   :1.0000000   Max.   :1.0000   Max.   :1.00000   Max.   :1.0000  
                                                                        
     Sport           Talk-Show            Thriller            War         
 Min.   :0.00000   Min.   :0.0000000   Min.   :0.00000   Min.   :0.00000  
 1st Qu.:0.00000   1st Qu.:0.0000000   1st Qu.:0.00000   1st Qu.:0.00000  
 Median :0.00000   Median :0.0000000   Median :0.00000   Median :0.00000  
 Mean   :0.01314   Mean   :0.0001001   Mean   :0.08438   Mean   :0.02676  
 3rd Qu.:0.00000   3rd Qu.:0.0000000   3rd Qu.:0.00000   3rd Qu.:0.00000  
 Max.   :1.00000   Max.   :1.0000000   Max.   :1.00000   Max.   :1.00000  
                                                                          
    Western       
 Min.   :0.00000  
 1st Qu.:0.00000  
 Median :0.00000  
 Mean   :0.03289  
 3rd Qu.:0.00000  
 Max.   :1.00000  
                  
print(head(df5))
              Title Gross Runtime Budget   Released Action Adult Adventure
1 39 Pounds of Love    NA      70     NA 2005-04-08      0     0         0
2              3:am    NA      21     NA 2005-01-25      0     0         0
3   500 Years Later    NA     106     NA 2005-02-24      0     0         0
4         5th World    NA      75     NA 2005-01-20      0     0         0
5                90    NA      14     NA 2005-03-12      0     0         0
6  Abel Raises Cain    NA      82     NA 2005-01-23      0     0         0
  Animation Biography Comedy Crime Documentary Drama Family Fantasy
1         0         1      0     0           1     0      0       0
2         0         0      0     0           0     0      0       0
3         0         0      0     0           1     0      0       0
4         0         0      0     0           0     1      0       0
5         0         0      0     0           0     0      0       0
6         0         1      0     0           1     0      0       0
  Film-Noir Game-Show History Horror Music Musical Mystery N/A News
1         0         0       0      0     0       0       0   0    0
2         0         0       0      0     0       0       0   0    0
3         0         0       0      0     0       0       0   0    0
4         0         0       0      0     0       0       0   0    0
5         0         0       0      0     0       0       0   0    0
6         0         0       0      0     0       0       0   0    0
  Reality-TV Romance Sci-Fi Short Sport Talk-Show Thriller War Western
1          0       1      0     0     0         0        0   0       0
2          0       0      0     1     0         0        1   0       0
3          0       0      0     0     0         0        0   0       0
4          0       1      0     0     0         0        0   0       0
5          0       0      0     1     0         0        0   1       0
6          0       0      0     0     0         0        0   0       0
ggplot(df, aes(Runtime, Gross)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")
Warning: Removed 35480 rows containing non-finite values (stat_summary).

ggplot(df, aes(Budget, Gross)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")
Warning: Removed 35442 rows containing non-finite values (stat_summary).

# Next melt the df to get genre as one column (like in question 3) and plot grouped by genre
df5.long = melt(df5, id.vars=c('Title', 'Gross', 'Runtime', 'Budget', 'Released'), variable.name='genre')

# Removing rows where value is 0 to answer the question
# This is because when value is 0, that means movie is not that genre.
df5.long = df5.long[apply(df5.long['value'], 1, function(z) !any(z==0)),]

library(plyr)

Attaching package: 'plyr'
The following object is masked from 'package:lubridate':

    here
The following object is masked from 'package:qdapTools':

    id
df5.long.runtime <- ddply(df5.long, c('Runtime', 'genre'), function(x) mean(x$Gross))
names(df5.long.runtime)[names(df5.long.runtime) == 'V1'] <- 'Gross'
df5.long.runtime = df5.long.runtime[!df5.long.runtime$Runtime > 180,]  # Removing movies greater than 3 hours

df5.long.budget <- ddply(df5.long, c('Budget', 'genre'), function(x) mean(x$Gross))
names(df5.long.budget)[names(df5.long.budget) == 'V1'] <- 'Gross'

ggplot(df5.long.runtime, aes(Runtime, Gross)) +
  geom_point(aes(color=genre, group=genre))
Warning: Removed 3327 rows containing missing values (geom_point).

ggplot(df5.long.budget, aes(Budget, Gross)) +
  geom_point(aes(color=genre, group=genre))
Warning: Removed 29 rows containing missing values (geom_point).

Q: Did you find any observable relationships or combinations of Budget/Runtime/Genre that result in high Gross revenue? If you divided the movies into different subsets, you may get different answers for them - point out interesting ones.

A:

Yes, it seems that in the aggregate, Gross revenue is positive correlated with Runtime, but only to a certain extent. After about 3 hours or so, the relationship is much murkier. Gross Revenue seems generally positively correlated with Budget.

# TODO: Investigate if Gross Revenue is related to Release Month
print(summary(df5.long))
    Title               Gross              Runtime      
 Length:79959       Min.   :0.000e+00   Min.   :  1.00  
 Class :character   1st Qu.:6.882e+06   1st Qu.: 69.00  
 Mode  :character   Median :3.457e+07   Median : 90.00  
                    Mean   :9.891e+07   Mean   : 81.15  
                    3rd Qu.:1.106e+08   3rd Qu.:102.00  
                    Max.   :2.784e+09   Max.   :873.00  
                    NA's   :68815       NA's   :1099    
     Budget             Released              genre           value  
 Min.   :     1100   Min.   :1893-05-09   Drama  :15832   Min.   :1  
 1st Qu.:  7000000   1st Qu.:1956-08-16   Comedy :12833   1st Qu.:1  
 Median : 20000000   Median :1989-04-28   Short  : 6516   Median :1  
 Mean   : 35134828   Mean   :1980-04-16   Romance: 4966   Mean   :1  
 3rd Qu.: 48000000   3rd Qu.:2001-12-13   Action : 4409   3rd Qu.:1  
 Max.   :425000000   Max.   :2018-08-01   Crime  : 4053   Max.   :1  
 NA's   :68815       NA's   :8537         (Other):31350              
print(head(df5.long))
                    Title Gross Runtime Budget   Released  genre value
23 Assault on Precinct 13    NA     109     NA 2005-01-19 Action     1
39            Boy s tenyu    NA     132     NA 2005-03-17 Action     1
41           Broadcast 23    NA       7     NA       <NA> Action     1
47               Chok-Dee    NA     105     NA 2005-02-16 Action     1
48                 Choker    NA      93     NA 2006-10-10 Action     1
69          Dust to Glory    NA      97     NA 2005-04-22 Action     1
df$released_month = month(df$Released)
ggplot(df, aes(released_month, Gross)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")
Warning: Removed 35487 rows containing non-finite values (stat_summary).

There is definitely a relationship of Gross Revenue and release month. Summer month movies and holiday movies gross significantly more revenue than movies released in other months.

6. Process Awards column

The variable Awards describes nominations and awards in text format. Convert it to 2 numeric columns, the first capturing the number of wins, and the second capturing nominations. Replace the Awards column with these new columns, and then study the relationship of Gross revenue with respect to them.

Note: The format of the Awards column is not standard; you may have to use regular expressions to find the relevant values. Try your best to process them, and you may leave the ones that don’t have enough information as NAs or set them to 0s.

# TODO: Convert Awards to 2 numeric columns: wins and nominations
library(stringi)
cols = c('Title', 'Awards', 'Gross')
df6 = df[cols]
print(unique(df6$Awards))
   [1] "3 wins."                                                             
   [2] "N/A"                                                                 
   [3] "2 wins."                                                             
   [4] "1 win."                                                              
   [5] "5 wins."                                                             
   [6] "5 wins & 4 nominations."                                             
   [7] "1 win & 1 nomination."                                               
   [8] "2 wins & 1 nomination."                                              
   [9] "1 nomination."                                                       
  [10] "1 win & 8 nominations."                                              
  [11] "1 win & 2 nominations."                                              
  [12] "1 win & 13 nominations."                                             
  [13] "15 nominations."                                                     
  [14] "2 nominations."                                                      
  [15] "28 wins & 3 nominations."                                            
  [16] "10 nominations."                                                     
  [17] "4 wins & 1 nomination."                                              
  [18] "6 wins."                                                             
  [19] "3 wins & 15 nominations."                                            
  [20] "2 wins & 9 nominations."                                             
  [21] "1 win & 5 nominations."                                              
  [22] "13 wins & 13 nominations."                                           
  [23] "4 wins."                                                             
  [24] "Nominated for 1 Oscar. Another 3 wins & 10 nominations."             
  [25] "4 wins & 7 nominations."                                             
  [26] "4 wins & 3 nominations."                                             
  [27] "9 nominations."                                                      
  [28] "3 wins & 2 nominations."                                             
  [29] "2 wins & 3 nominations."                                             
  [30] "16 nominations."                                                     
  [31] "10 wins & 13 nominations."                                           
  [32] "4 nominations."                                                      
  [33] "6 nominations."                                                      
  [34] "4 wins & 24 nominations."                                            
  [35] "5 wins & 10 nominations."                                            
  [36] "5 wins & 2 nominations."                                             
  [37] "7 nominations."                                                      
  [38] "4 wins & 13 nominations."                                            
  [39] "1 win & 3 nominations."                                              
  [40] "21 wins & 15 nominations."                                           
  [41] "2 wins & 5 nominations."                                             
  [42] "1 win & 10 nominations."                                             
  [43] "3 wins & 4 nominations."                                             
  [44] "3 nominations."                                                      
  [45] "4 wins & 8 nominations."                                             
  [46] "Nominated for 1 Oscar. Another 13 wins & 15 nominations."            
  [47] "Nominated for 1 BAFTA Film Award. Another 6 wins & 3 nominations."   
  [48] "6 wins & 5 nominations."                                             
  [49] "2 wins & 19 nominations."                                            
  [50] "12 wins & 1 nomination."                                             
  [51] "31 wins & 47 nominations."                                           
  [52] "Won 1 Oscar. Another 5 wins & 2 nominations."                        
  [53] "7 wins & 12 nominations."                                            
  [54] "4 wins & 5 nominations."                                             
  [55] "Nominated for 2 Oscars. Another 31 wins & 22 nominations."           
  [56] "Won 1 Oscar. Another 7 wins & 1 nomination."                         
  [57] "4 wins & 2 nominations."                                             
  [58] "3 wins & 24 nominations."                                            
  [59] "10 wins & 2 nominations."                                            
  [60] "3 wins & 1 nomination."                                              
  [61] "11 nominations."                                                     
  [62] "32 wins & 65 nominations."                                           
  [63] "6 wins & 9 nominations."                                             
  [64] "6 wins & 1 nomination."                                              
  [65] "6 wins & 10 nominations."                                            
  [66] "15 wins & 6 nominations."                                            
  [67] "3 wins & 12 nominations."                                            
  [68] "Won 1 Golden Globe. Another 5 wins & 7 nominations."                 
  [69] "Nominated for 1 Primetime Emmy. Another 1 win & 1 nomination."       
  [70] "1 win & 11 nominations."                                             
  [71] "Nominated for 1 Oscar. Another 7 wins & 8 nominations."              
  [72] "10 wins."                                                            
  [73] "5 wins & 6 nominations."                                             
  [74] "2 wins & 10 nominations."                                            
  [75] "Nominated for 1 Oscar. Another 9 wins & 27 nominations."             
  [76] "Nominated for 1 Oscar. Another 8 wins & 18 nominations."             
  [77] "2 wins & 2 nominations."                                             
  [78] "Nominated for 1 Golden Globe. Another 4 nominations."                
  [79] "Nominated for 1 Golden Globe. Another 2 wins & 16 nominations."      
  [80] "Nominated for 5 Oscars. Another 8 wins & 26 nominations."            
  [81] "1 win & 4 nominations."                                              
  [82] "Won 1 Oscar. Another 20 wins & 5 nominations."                       
  [83] "13 wins & 9 nominations."                                            
  [84] "5 wins & 1 nomination."                                              
  [85] "Nominated for 1 Golden Globe. Another 7 nominations."                
  [86] "8 wins & 17 nominations."                                            
  [87] "8 wins & 9 nominations."                                             
  [88] "3 wins & 3 nominations."                                             
  [89] "9 wins & 7 nominations."                                             
  [90] "13 wins & 4 nominations."                                            
  [91] "8 wins & 6 nominations."                                             
  [92] "2 wins & 6 nominations."                                             
  [93] "9 wins."                                                             
  [94] "11 wins & 2 nominations."                                            
  [95] "5 nominations."                                                      
  [96] "6 wins & 8 nominations."                                             
  [97] "Nominated for 1 Golden Globe. Another 1 win & 2 nominations."        
  [98] "Nominated for 1 BAFTA Film Award. Another 8 wins & 3 nominations."   
  [99] "Nominated for 2 Oscars. Another 22 wins & 64 nominations."           
 [100] "9 wins & 9 nominations."                                             
 [101] "Won 3 Oscars. Another 61 wins & 105 nominations."                    
 [102] "5 wins & 5 nominations."                                             
 [103] "Nominated for 1 Oscar. Another 14 wins & 4 nominations."             
 [104] "1 win & 16 nominations."                                             
 [105] "Nominated for 2 Golden Globes. Another 1 win & 11 nominations."      
 [106] "9 wins & 4 nominations."                                             
 [107] "Won 3 Primetime Emmys. Another 1 win & 2 nominations."               
 [108] "2 wins & 7 nominations."                                             
 [109] "11 wins & 22 nominations."                                           
 [110] "13 wins."                                                            
 [111] "7 wins & 1 nomination."                                              
 [112] "6 wins & 3 nominations."                                             
 [113] "4 wins & 12 nominations."                                            
 [114] "18 wins & 3 nominations."                                            
 [115] "Won 1 Oscar. Another 67 wins & 103 nominations."                     
 [116] "Nominated for 1 Oscar. Another 5 wins."                              
 [117] "29 wins & 15 nominations."                                           
 [118] "5 wins & 3 nominations."                                             
 [119] "7 wins & 2 nominations."                                             
 [120] "Won 1 Oscar. Another 20 wins & 77 nominations."                      
 [121] "8 wins & 15 nominations."                                            
 [122] "4 wins & 4 nominations."                                             
 [123] "Won 2 Oscars. Another 72 wins & 57 nominations."                     
 [124] "13 wins & 37 nominations."                                           
 [125] "Nominated for 1 Oscar. Another 1 win & 1 nomination."                
 [126] "Nominated for 1 Oscar. Another 4 wins."                              
 [127] "Nominated for 1 Oscar. Another 1 nomination."                        
 [128] "Nominated for 2 Oscars. Another 14 wins & 42 nominations."           
 [129] "2 wins & 4 nominations."                                             
 [130] "3 wins & 22 nominations."                                            
 [131] "Nominated for 1 Golden Globe. Another 3 nominations."                
 [132] "Nominated for 1 Golden Globe. Another 2 nominations."                
 [133] "Nominated for 3 Oscars. Another 16 wins & 42 nominations."           
 [134] "13 wins & 24 nominations."                                           
 [135] "3 wins & 10 nominations."                                            
 [136] "Nominated for 1 Oscar. Another 1 win & 13 nominations."              
 [137] "19 wins & 5 nominations."                                            
 [138] "Nominated for 1 Primetime Emmy. Another 2 wins & 5 nominations."     
 [139] "12 wins & 6 nominations."                                            
 [140] "6 wins & 11 nominations."                                            
 [141] "Won 1 Oscar. Another 13 wins & 30 nominations."                      
 [142] "11 wins & 9 nominations."                                            
 [143] "1 win & 6 nominations."                                              
 [144] "Won 1 Primetime Emmy. Another 2 nominations."                        
 [145] "20 nominations."                                                     
 [146] "Nominated for 2 Golden Globes. Another 21 wins & 72 nominations."    
 [147] "Nominated for 1 Oscar. Another 17 wins & 48 nominations."            
 [148] "Nominated for 1 Oscar. Another 5 wins & 1 nomination."               
 [149] "12 wins & 5 nominations."                                            
 [150] "8 wins & 34 nominations."                                            
 [151] "1 win & 7 nominations."                                              
 [152] "3 wins & 6 nominations."                                             
 [153] "Nominated for 1 Oscar. Another 40 wins & 32 nominations."            
 [154] "7 wins & 23 nominations."                                            
 [155] "6 wins & 18 nominations."                                            
 [156] "1 win & 9 nominations."                                              
 [157] "Won 1 Oscar. Another 2 wins."                                        
 [158] "Won 4 Oscars. Another 60 wins & 81 nominations."                     
 [159] "Nominated for 1 BAFTA Film Award. Another 12 wins & 13 nominations." 
 [160] "8 wins & 1 nomination."                                              
 [161] "9 wins & 19 nominations."                                            
 [162] "5 wins & 8 nominations."                                             
 [163] "10 wins & 22 nominations."                                           
 [164] "3 wins & 9 nominations."                                             
 [165] "6 wins & 4 nominations."                                             
 [166] "Nominated for 1 BAFTA Film Award. Another 1 nomination."             
 [167] "5 wins & 9 nominations."                                             
 [168] "2 wins & 8 nominations."                                             
 [169] "13 wins & 7 nominations."                                            
 [170] "3 wins & 14 nominations."                                            
 [171] "6 wins & 6 nominations."                                             
 [172] "8 wins."                                                             
 [173] "20 wins & 15 nominations."                                           
 [174] "11 wins & 1 nomination."                                             
 [175] "12 wins."                                                            
 [176] "7 wins & 4 nominations."                                             
 [177] "7 wins."                                                             
 [178] "3 wins & 5 nominations."                                             
 [179] "3 wins & 7 nominations."                                             
 [180] "8 wins & 2 nominations."                                             
 [181] "9 wins & 1 nomination."                                              
 [182] "Nominated for 1 Golden Globe. Another 12 wins & 7 nominations."      
 [183] "Nominated for 1 Golden Globe. Another 4 wins & 1 nomination."        
 [184] "8 nominations."                                                      
 [185] "Won 1 Oscar. Another 26 wins & 2 nominations."                       
 [186] "7 wins & 8 nominations."                                             
 [187] "8 wins & 10 nominations."                                            
 [188] "Nominated for 3 BAFTA Film Awards. Another 2 wins & 2 nominations."  
 [189] "Nominated for 1 Oscar. Another 3 wins & 15 nominations."             
 [190] "13 wins & 20 nominations."                                           
 [191] "Nominated for 2 Oscars. Another 16 wins & 48 nominations."           
 [192] "Won 1 Oscar. Another 120 wins & 82 nominations."                     
 [193] "Nominated for 1 Oscar. Another 1 win."                               
 [194] "7 wins & 17 nominations."                                            
 [195] "41 wins & 14 nominations."                                           
 [196] "Nominated for 1 Golden Globe. Another 5 wins & 10 nominations."      
 [197] "Won 1 Oscar. Another 23 wins & 54 nominations."                      
 [198] "14 wins & 20 nominations."                                           
 [199] "Nominated for 1 Oscar. Another 7 wins & 11 nominations."             
 [200] "12 wins & 23 nominations."                                           
 [201] "11 wins."                                                            
 [202] "Nominated for 2 Oscars. Another 7 nominations."                      
 [203] "6 wins & 24 nominations."                                            
 [204] "Nominated for 1 Oscar. Another 3 wins & 19 nominations."             
 [205] "Won 3 Primetime Emmys. Another 1 win & 5 nominations."               
 [206] "10 wins & 4 nominations."                                            
 [207] "2 wins & 17 nominations."                                            
 [208] "21 wins & 29 nominations."                                           
 [209] "4 wins & 6 nominations."                                             
 [210] "Nominated for 3 Oscars. Another 40 wins & 37 nominations."           
 [211] "4 wins & 10 nominations."                                            
 [212] "10 wins & 1 nomination."                                             
 [213] "22 wins & 5 nominations."                                            
 [214] "6 wins & 7 nominations."                                             
 [215] "6 wins & 2 nominations."                                             
 [216] "12 wins & 14 nominations."                                           
 [217] "3 wins & 13 nominations."                                            
 [218] "Nominated for 2 Oscars. Another 29 wins & 71 nominations."           
 [219] "3 wins & 18 nominations."                                            
 [220] "Nominated for 1 Oscar. Another 1 win & 5 nominations."               
 [221] "Nominated for 1 Oscar. Another 31 wins & 47 nominations."            
 [222] "Won 3 Oscars. Another 80 wins & 121 nominations."                    
 [223] "12 wins & 2 nominations."                                            
 [224] "5 wins & 12 nominations."                                            
 [225] "Nominated for 1 Golden Globe. Another 1 win & 11 nominations."       
 [226] "10 wins & 11 nominations."                                           
 [227] "Nominated for 1 Oscar. Another 1 win & 62 nominations."              
 [228] "15 wins & 4 nominations."                                            
 [229] "Nominated for 3 Oscars. Another 3 wins."                             
 [230] "Nominated for 1 Oscar. Another 2 wins & 19 nominations."             
 [231] "Nominated for 1 BAFTA Film Award. Another 1 win."                    
 [232] "Nominated for 1 Golden Globe. Another 2 wins & 14 nominations."      
 [233] "Nominated for 1 Oscar. Another 24 wins & 13 nominations."            
 [234] "13 wins & 26 nominations."                                           
 [235] "Nominated for 1 Oscar. Another 7 wins & 7 nominations."              
 [236] "Won 1 Oscar. Another 17 wins & 92 nominations."                      
 [237] "13 wins & 2 nominations."                                            
 [238] "5 wins & 17 nominations."                                            
 [239] "Won 1 Oscar. Another 12 wins & 6 nominations."                       
 [240] "21 wins & 6 nominations."                                            
 [241] "Nominated for 1 Oscar. Another 6 wins & 1 nomination."               
 [242] "20 wins & 25 nominations."                                           
 [243] "Won 2 Oscars. Another 7 wins & 37 nominations."                      
 [244] "3 wins & 8 nominations."                                             
 [245] "8 wins & 11 nominations."                                            
 [246] "8 wins & 12 nominations."                                            
 [247] "Nominated for 2 Oscars. Another 7 wins & 3 nominations."             
 [248] "24 wins & 2 nominations."                                            
 [249] "Nominated for 1 Oscar. Another 3 wins & 1 nomination."               
 [250] "17 wins."                                                            
 [251] "18 wins & 2 nominations."                                            
 [252] "Won 1 Oscar. Another 46 wins & 59 nominations."                      
 [253] "Nominated for 1 Golden Globe. Another 5 wins & 9 nominations."       
 [254] "2 wins & 15 nominations."                                            
 [255] "3 wins & 26 nominations."                                            
 [256] "6 wins & 13 nominations."                                            
 [257] "9 wins & 22 nominations."                                            
 [258] "Nominated for 3 Oscars. Another 17 wins & 41 nominations."           
 [259] "10 wins & 19 nominations."                                           
 [260] "10 wins & 9 nominations."                                            
 [261] "Nominated for 1 Golden Globe. Another 32 wins & 17 nominations."     
 [262] "Won 1 Oscar. Another 19 wins."                                       
 [263] "5 wins & 7 nominations."                                             
 [264] "Nominated for 1 Golden Globe. Another 2 wins & 3 nominations."       
 [265] "Nominated for 3 Oscars. Another 12 wins & 37 nominations."           
 [266] "Nominated for 2 Oscars. Another 11 wins & 39 nominations."           
 [267] "20 wins & 12 nominations."                                           
 [268] "4 wins & 9 nominations."                                             
 [269] "Won 1 BAFTA Film Award. Another 4 wins & 1 nomination."              
 [270] "32 wins & 39 nominations."                                           
 [271] "Nominated for 1 Golden Globe. Another 25 wins & 84 nominations."     
 [272] "25 wins & 30 nominations."                                           
 [273] "Won 1 Oscar. Another 93 wins & 118 nominations."                     
 [274] "Nominated for 2 Golden Globes. Another 10 wins & 26 nominations."    
 [275] "31 wins & 11 nominations."                                           
 [276] "14 nominations."                                                     
 [277] "10 wins & 5 nominations."                                            
 [278] "Won 2 Oscars. Another 21 wins & 80 nominations."                     
 [279] "5 wins & 13 nominations."                                            
 [280] "Nominated for 1 Golden Globe. Another 5 nominations."                
 [281] "Won 1 Oscar. Another 26 wins & 23 nominations."                      
 [282] "Nominated for 1 Oscar. Another 3 wins."                              
 [283] "16 wins & 15 nominations."                                           
 [284] "Won 2 Oscars. Another 53 wins & 131 nominations."                    
 [285] "Nominated for 1 Oscar. Another 3 wins & 3 nominations."              
 [286] "12 wins & 12 nominations."                                           
 [287] "9 wins & 5 nominations."                                             
 [288] "26 wins & 16 nominations."                                           
 [289] "Won 1 Golden Globe. Another 14 wins & 11 nominations."               
 [290] "11 wins & 19 nominations."                                           
 [291] "7 wins & 3 nominations."                                             
 [292] "7 wins & 6 nominations."                                             
 [293] "17 wins & 1 nomination."                                             
 [294] "Nominated for 1 Oscar. Another 15 wins & 21 nominations."            
 [295] "Nominated for 5 Oscars. Another 31 wins & 88 nominations."           
 [296] "12 wins & 10 nominations."                                           
 [297] "Nominated for 1 BAFTA Film Award. Another 2 wins & 1 nomination."    
 [298] "13 wins & 3 nominations."                                            
 [299] "8 wins & 3 nominations."                                             
 [300] "Nominated for 1 Oscar. Another 4 wins & 16 nominations."             
 [301] "11 wins & 5 nominations."                                            
 [302] "Nominated for 7 Oscars. Another 6 wins & 36 nominations."            
 [303] "Nominated for 2 Oscars. Another 7 wins & 11 nominations."            
 [304] "Nominated for 1 Golden Globe. Another 11 wins & 27 nominations."     
 [305] "Nominated for 3 Oscars. Another 38 wins & 66 nominations."           
 [306] "Nominated for 1 Oscar. Another 7 wins & 12 nominations."             
 [307] "14 wins & 6 nominations."                                            
 [308] "Won 4 Oscars. Another 6 wins & 11 nominations."                      
 [309] "Nominated for 1 Primetime Emmy. Another 2 nominations."              
 [310] "Nominated for 3 Oscars. Another 23 wins & 18 nominations."           
 [311] "2 wins & 18 nominations."                                            
 [312] "4 wins & 15 nominations."                                            
 [313] "9 wins & 10 nominations."                                            
 [314] "5 wins & 24 nominations."                                            
 [315] "Nominated for 1 Oscar. Another 14 wins & 45 nominations."            
 [316] "9 wins & 13 nominations."                                            
 [317] "15 wins & 10 nominations."                                           
 [318] "Nominated for 1 Oscar. Another 37 wins & 43 nominations."            
 [319] "Nominated for 1 Oscar. Another 12 wins & 1 nomination."              
 [320] "Won 1 Oscar. Another 3 wins."                                        
 [321] "Nominated for 3 Oscars. Another 13 wins & 25 nominations."           
 [322] "Nominated for 4 Primetime Emmys. Another 1 win & 3 nominations."     
 [323] "8 wins & 5 nominations."                                             
 [324] "Nominated for 1 Golden Globe. Another 3 wins & 13 nominations."      
 [325] "24 wins & 3 nominations."                                            
 [326] "7 wins & 13 nominations."                                            
 [327] "Nominated for 1 Golden Globe. Another 5 wins & 7 nominations."       
 [328] "Won 1 Oscar. Another 21 wins & 1 nomination."                        
 [329] "7 wins & 16 nominations."                                            
 [330] "Nominated for 1 Oscar. Another 4 wins & 10 nominations."             
 [331] "Nominated for 1 Oscar. Another 13 wins."                             
 [332] "Nominated for 1 Golden Globe. Another 4 wins & 14 nominations."      
 [333] "9 wins & 27 nominations."                                            
 [334] "Won 1 Oscar. Another 12 wins & 21 nominations."                      
 [335] "Nominated for 2 Oscars. Another 24 wins & 67 nominations."           
 [336] "Nominated for 1 Oscar. Another 11 wins & 28 nominations."            
 [337] "Won 1 Oscar. Another 60 wins & 95 nominations."                      
 [338] "11 wins & 7 nominations."                                            
 [339] "19 wins & 21 nominations."                                           
 [340] "3 wins & 11 nominations."                                            
 [341] "12 wins & 13 nominations."                                           
 [342] "5 wins & 21 nominations."                                            
 [343] "1 win & 17 nominations."                                             
 [344] "Nominated for 1 Golden Globe. Another 17 wins & 18 nominations."     
 [345] "1 win & 12 nominations."                                             
 [346] "Won 1 Oscar. Another 87 wins & 244 nominations."                     
 [347] "Won 1 Oscar. Another 38 wins & 11 nominations."                      
 [348] "7 wins & 9 nominations."                                             
 [349] "2 wins & 11 nominations."                                            
 [350] "Won 3 Oscars. Another 4 wins & 8 nominations."                       
 [351] "4 wins & 11 nominations."                                            
 [352] "Nominated for 1 Primetime Emmy. Another 2 wins & 1 nomination."      
 [353] "Won 1 Oscar. Another 1 win & 9 nominations."                         
 [354] "Won 1 Oscar. Another 3 wins & 6 nominations."                        
 [355] "Won 6 Oscars. Another 49 wins & 121 nominations."                    
 [356] "16 wins & 10 nominations."                                           
 [357] "7 wins & 11 nominations."                                            
 [358] "2 wins & 14 nominations."                                            
 [359] "21 wins & 36 nominations."                                           
 [360] "Nominated for 1 Oscar. Another 2 wins & 2 nominations."              
 [361] "Nominated for 1 Oscar. Another 2 wins & 4 nominations."              
 [362] "Nominated for 1 BAFTA Film Award. Another 44 wins & 7 nominations."  
 [363] "Nominated for 1 Golden Globe. Another 6 wins & 33 nominations."      
 [364] "Nominated for 1 Oscar. Another 17 wins & 25 nominations."            
 [365] "Won 1 Oscar. Another 13 wins & 19 nominations."                      
 [366] "Nominated for 1 Oscar. Another 73 wins & 162 nominations."           
 [367] "12 nominations."                                                     
 [368] "20 wins & 14 nominations."                                           
 [369] "8 wins & 8 nominations."                                             
 [370] "Nominated for 4 Oscars. Another 100 wins & 86 nominations."          
 [371] "17 wins & 5 nominations."                                            
 [372] "Won 2 Oscars. Another 14 wins & 42 nominations."                     
 [373] "Nominated for 10 Oscars. Another 47 wins & 117 nominations."         
 [374] "Nominated for 3 BAFTA Film Awards. Another 11 wins & 39 nominations."
 [375] "9 wins & 8 nominations."                                             
 [376] "Nominated for 1 Oscar. Another 5 wins & 28 nominations."             
 [377] "Nominated for 2 Golden Globes. Another 7 wins & 15 nominations."     
 [378] "Nominated for 3 Oscars. Another 27 wins & 55 nominations."           
 [379] "Won 1 BAFTA Film Award. Another 6 wins & 8 nominations."             
 [380] "Nominated for 3 Oscars. Another 2 wins & 12 nominations."            
 [381] "Won 1 Primetime Emmy. Another 5 nominations."                        
 [382] "11 wins & 4 nominations."                                            
 [383] "Nominated for 1 Oscar. Another 10 wins & 27 nominations."            
 [384] "Nominated for 1 Oscar. Another 18 wins & 76 nominations."            
 [385] "10 wins & 16 nominations."                                           
 [386] "9 wins & 2 nominations."                                             
 [387] "Nominated for 1 Oscar. Another 20 wins & 27 nominations."            
 [388] "Nominated for 1 Golden Globe. Another 1 win & 4 nominations."        
 [389] "6 wins & 14 nominations."                                            
 [390] "6 wins & 21 nominations."                                            
 [391] "Won 1 Oscar. Another 83 wins & 96 nominations."                      
 [392] "Won 2 Oscars. Another 3 wins & 1 nomination."                        
 [393] "Nominated for 1 Golden Globe. Another 14 wins & 34 nominations."     
 [394] "Nominated for 1 Golden Globe. Another 23 wins & 23 nominations."     
 [395] "19 wins & 10 nominations."                                           
 [396] "Won 6 Oscars. Another 206 wins & 189 nominations."                   
 [397] "Won 1 Oscar. Another 20 wins & 75 nominations."                      
 [398] "10 wins & 14 nominations."                                           
 [399] "13 wins & 6 nominations."                                            
 [400] "Nominated for 1 BAFTA Film Award. Another 1 win & 1 nomination."     
 [401] "Nominated for 1 Golden Globe. Another 15 wins & 25 nominations."     
 [402] "3 wins & 30 nominations."                                            
 [403] "9 wins & 3 nominations."                                             
 [404] "Nominated for 2 Oscars. Another 14 wins & 56 nominations."           
 [405] "Nominated for 1 Oscar. Another 8 wins & 19 nominations."             
 [406] "Nominated for 1 Oscar. Another 8 wins & 1 nomination."               
 [407] "Nominated for 1 Oscar. Another 16 wins & 53 nominations."            
 [408] "Nominated for 1 Oscar. Another 3 wins & 13 nominations."             
 [409] "Nominated for 1 Oscar. Another 6 wins & 20 nominations."             
 [410] "Nominated for 1 Oscar. Another 32 wins & 32 nominations."            
 [411] "Nominated for 1 Oscar. Another 2 wins."                              
 [412] "10 wins & 21 nominations."                                           
 [413] "10 wins & 3 nominations."                                            
 [414] "Won 1 BAFTA Film Award. Another 4 wins."                             
 [415] "Nominated for 2 Oscars. Another 1 win & 2 nominations."              
 [416] "Nominated for 2 Oscars. Another 10 wins & 25 nominations."           
 [417] "24 wins & 16 nominations."                                           
 [418] "1 win & 14 nominations."                                             
 [419] "Nominated for 2 Golden Globes. Another 4 wins & 5 nominations."      
 [420] "13 wins & 5 nominations."                                            
 [421] "Won 1 Oscar. Another 53 wins & 75 nominations."                      
 [422] "Nominated for 1 Oscar. Another 8 wins & 29 nominations."             
 [423] "Nominated for 1 Oscar. Another 31 wins & 4 nominations."             
 [424] "11 wins & 15 nominations."                                           
 [425] "Nominated for 3 Oscars. Another 6 wins & 10 nominations."            
 [426] "5 wins & 16 nominations."                                            
 [427] "3 wins & 16 nominations."                                            
 [428] "Nominated for 1 Oscar. Another 7 wins & 9 nominations."              
 [429] "Nominated for 4 Oscars. Another 65 wins & 37 nominations."           
 [430] "6 wins & 33 nominations."                                            
 [431] "Nominated for 2 Oscars. Another 1 nomination."                       
 [432] "Won 1 Oscar. Another 31 wins & 73 nominations."                      
 [433] "7 wins & 15 nominations."                                            
 [434] "Nominated for 3 Oscars. Another 17 wins & 60 nominations."           
 [435] "Won 1 Oscar. Another 1 win & 4 nominations."                         
 [436] "Nominated for 1 Golden Globe. Another 28 wins & 32 nominations."     
 [437] "16 wins & 5 nominations."                                            
 [438] "Nominated for 1 Oscar. Another 37 wins & 34 nominations."            
 [439] "Nominated for 1 BAFTA Film Award. Another 10 wins & 1 nomination."   
 [440] "Nominated for 1 Oscar. Another 8 wins & 10 nominations."             
 [441] "Nominated for 5 Oscars. Another 38 wins & 69 nominations."           
 [442] "Nominated for 1 Oscar. Another 5 nominations."                       
 [443] "22 wins & 43 nominations."                                           
 [444] "Nominated for 1 Oscar. Another 1 win & 4 nominations."               
 [445] "20 wins & 18 nominations."                                           
 [446] "Nominated for 1 Oscar. Another 44 wins & 19 nominations."            
 [447] "36 wins & 22 nominations."                                           
 [448] "7 wins & 7 nominations."                                             
 [449] "Nominated for 2 Golden Globes. Another 6 wins & 10 nominations."     
 [450] "Nominated for 1 Golden Globe. Another 2 wins & 8 nominations."       
 [451] "Nominated for 1 Oscar. Another 6 wins & 6 nominations."              
 [452] "Nominated for 1 Oscar. Another 6 wins & 5 nominations."              
 [453] "Nominated for 1 Golden Globe. Another 6 wins & 11 nominations."      
 [454] "Won 1 Oscar. Another 14 wins & 22 nominations."                      
 [455] "Won 1 Oscar. Another 13 wins & 38 nominations."                      
 [456] "Won 2 Oscars. Another 81 wins & 130 nominations."                    
 [457] "8 wins & 4 nominations."                                             
 [458] "11 wins & 6 nominations."                                            
 [459] "Won 1 Oscar. Another 27 wins & 20 nominations."                      
 [460] "Won 1 Golden Globe. Another 1 win & 4 nominations."                  
 [461] "4 wins & 20 nominations."                                            
 [462] "Nominated for 1 Oscar. Another 11 wins & 21 nominations."            
 [463] "Nominated for 1 Oscar. Another 2 wins & 1 nomination."               
 [464] "Won 1 Oscar. Another 10 wins & 50 nominations."                      
 [465] "Nominated for 2 BAFTA Film Awards. Another 10 wins & 25 nominations."
 [466] "Nominated for 1 Oscar. Another 13 wins & 3 nominations."             
 [467] "Nominated for 1 Golden Globe. Another 3 wins & 17 nominations."      
 [468] "7 wins & 5 nominations."                                             
 [469] "Won 1 BAFTA Film Award. Another 1 win."                              
 [470] "Won 1 Oscar. Another 37 wins & 59 nominations."                      
 [471] "10 wins & 6 nominations."                                            
 [472] "Nominated for 2 Oscars. Another 2 wins & 3 nominations."             
 [473] "Nominated for 1 Oscar. Another 9 wins & 39 nominations."             
 [474] "Nominated for 1 Primetime Emmy. Another 1 win & 5 nominations."      
 [475] "8 wins & 7 nominations."                                             
 [476] "14 wins & 7 nominations."                                            
 [477] "Nominated for 1 Oscar. Another 4 wins & 33 nominations."             
 [478] "5 wins & 20 nominations."                                            
 [479] "Nominated for 1 Oscar. Another 6 wins & 3 nominations."              
 [480] "Won 1 Oscar. Another 19 wins & 46 nominations."                      
 [481] "Nominated for 1 Oscar. Another 1 win & 6 nominations."               
 [482] "Won 4 Oscars. Another 29 wins & 23 nominations."                     
 [483] "2 wins & 12 nominations."                                            
 [484] "Nominated for 1 Golden Globe. Another 1 win & 13 nominations."       
 [485] "Won 1 Oscar. Another 52 wins & 102 nominations."                     
 [486] "12 wins & 7 nominations."                                            
 [487] "Nominated for 1 Oscar. Another 54 wins & 22 nominations."            
 [488] "Nominated for 1 Oscar. Another 15 wins & 20 nominations."            
 [489] "Nominated for 1 Oscar. Another 12 wins & 22 nominations."            
 [490] "Nominated for 1 Golden Globe. Another 11 wins & 13 nominations."     
 [491] "Nominated for 3 Oscars. Another 54 wins & 63 nominations."           
 [492] "Won 1 Oscar. Another 3 wins & 1 nomination."                         
 [493] "10 wins & 12 nominations."                                           
 [494] "Nominated for 2 Oscars. Another 15 wins & 33 nominations."           
 [495] "13 wins & 27 nominations."                                           
 [496] "Nominated for 1 Golden Globe. Another 23 wins & 26 nominations."     
 [497] "Nominated for 1 Oscar. Another 4 wins & 2 nominations."              
 [498] "Nominated for 5 Oscars. Another 7 wins & 30 nominations."            
 [499] "6 wins & 12 nominations."                                            
 [500] "Nominated for 2 Oscars. Another 19 wins & 45 nominations."           
 [501] "Nominated for 1 Oscar. Another 1 win & 2 nominations."               
 [502] "Nominated for 1 Oscar. Another 33 wins & 45 nominations."            
 [503] "Nominated for 1 Primetime Emmy. Another 2 wins & 6 nominations."     
 [504] "9 wins & 15 nominations."                                            
 [505] "Nominated for 1 Oscar. Another 2 nominations."                       
 [506] "Won 1 Oscar. Another 31 wins & 57 nominations."                      
 [507] "Won 1 Oscar. Another 21 wins & 2 nominations."                       
 [508] "4 wins & 18 nominations."                                            
 [509] "Nominated for 1 Golden Globe. Another 2 wins & 7 nominations."       
 [510] "Nominated for 1 Oscar. Another 28 wins & 55 nominations."            
 [511] "15 wins & 16 nominations."                                           
 [512] "Won 5 Oscars. Another 53 wins & 101 nominations."                    
 [513] "Won 3 Oscars. Another 1 win & 13 nominations."                       
 [514] "Nominated for 1 Primetime Emmy. Another 5 nominations."              
 [515] "Nominated for 1 Golden Globe. Another 3 wins & 21 nominations."      
 [516] "Won 1 Oscar. Another 17 wins & 35 nominations."                      
 [517] "16 wins."                                                            
 [518] "Won 1 Oscar. Another 6 wins & 6 nominations."                        
 [519] "Nominated for 1 BAFTA Film Award. Another 3 wins."                   
 [520] "Nominated for 1 Oscar. Another 5 wins & 18 nominations."             
 [521] "30 wins & 12 nominations."                                           
 [522] "Won 1 Oscar. Another 23 wins & 35 nominations."                      
 [523] "Nominated for 1 Oscar. Another 10 wins & 6 nominations."             
 [524] "12 wins & 15 nominations."                                           
 [525] "Nominated for 1 Oscar. Another 7 wins & 14 nominations."             
 [526] "Nominated for 2 Oscars. Another 54 wins & 55 nominations."           
 [527] "Nominated for 2 Golden Globes. Another 7 wins & 5 nominations."      
 [528] "8 wins & 20 nominations."                                            
 [529] "10 wins & 18 nominations."                                           
 [530] "Won 1 Golden Globe. Another 4 wins & 14 nominations."                
 [531] "Nominated for 2 Oscars. Another 7 wins & 35 nominations."            
 [532] "Won 1 Primetime Emmy. Another 3 wins & 2 nominations."               
 [533] "Won 5 Oscars. Another 30 wins & 13 nominations."                     
 [534] "Won 1 Oscar. Another 2 wins & 9 nominations."                        
 [535] "Nominated for 3 Oscars. Another 18 wins & 41 nominations."           
 [536] "11 wins & 10 nominations."                                           
 [537] "Nominated for 1 Oscar. Another 17 wins & 1 nomination."              
 [538] "Nominated for 1 Oscar. Another 32 wins & 61 nominations."            
 [539] "17 wins & 11 nominations."                                           
 [540] "Nominated for 1 Golden Globe. Another 4 wins & 8 nominations."       
 [541] "Nominated for 1 Oscar. Another 21 wins & 20 nominations."            
 [542] "Nominated for 2 Oscars. Another 14 wins & 23 nominations."           
 [543] "Nominated for 1 Golden Globe. Another 1 win & 3 nominations."        
 [544] "Nominated for 1 Oscar. Another 3 wins & 4 nominations."              
 [545] "Nominated for 1 Oscar. Another 4 wins & 6 nominations."              
 [546] "Won 2 Oscars. Another 14 wins & 16 nominations."                     
 [547] "Nominated for 1 Primetime Emmy. Another 2 wins."                     
 [548] "Won 4 Oscars. Another 69 wins & 83 nominations."                     
 [549] "Won 1 Oscar. Another 2 wins & 10 nominations."                       
 [550] "Nominated for 1 BAFTA Film Award. Another 2 wins & 3 nominations."   
 [551] "13 wins & 21 nominations."                                           
 [552] "Nominated for 2 Oscars. Another 30 wins & 41 nominations."           
 [553] "Won 5 Oscars. Another 103 wins & 96 nominations."                    
 [554] "Nominated for 1 Primetime Emmy. Another 4 wins & 2 nominations."     
 [555] "9 wins & 14 nominations."                                            
 [556] "Nominated for 1 Oscar. Another 5 wins & 11 nominations."             
 [557] "Nominated for 2 Oscars. Another 12 nominations."                     
 [558] "21 wins & 2 nominations."                                            
 [559] "Nominated for 1 Oscar. Another 17 wins & 28 nominations."            
 [560] "Won 1 Oscar. Another 41 wins & 128 nominations."                     
 [561] "Won 1 Golden Globe. Another 1 nomination."                           
 [562] "Nominated for 3 Oscars. Another 48 wins & 75 nominations."           
 [563] "2 wins & 16 nominations."                                            
 [564] "Nominated for 1 Oscar. Another 2 wins & 6 nominations."              
 [565] "Won 1 Oscar. Another 47 wins & 37 nominations."                      
 [566] "Nominated for 1 Oscar. Another 20 wins & 10 nominations."            
 [567] "17 wins & 16 nominations."                                           
 [568] "Nominated for 4 BAFTA Film Awards. Another 16 wins & 8 nominations." 
 [569] "Nominated for 1 Oscar. Another 16 wins & 33 nominations."            
 [570] "Won 1 Oscar. Another 2 wins & 7 nominations."                        
 [571] "Nominated for 1 Golden Globe. Another 7 wins & 24 nominations."      
 [572] "5 wins & 14 nominations."                                            
 [573] "Nominated for 1 Oscar. Another 10 wins & 31 nominations."            
 [574] "6 wins & 22 nominations."                                            
 [575] "7 wins & 14 nominations."                                            
 [576] "21 wins & 17 nominations."                                           
 [577] "Nominated for 1 Oscar. Another 8 wins."                              
 [578] "Won 1 Oscar. Another 7 wins & 11 nominations."                       
 [579] "10 wins & 10 nominations."                                           
 [580] "26 wins & 2 nominations."                                            
 [581] "4 wins & 14 nominations."                                            
 [582] "Nominated for 1 Oscar. Another 9 wins & 2 nominations."              
 [583] "10 wins & 7 nominations."                                            
 [584] "Nominated for 3 Oscars. Another 28 wins & 51 nominations."           
 [585] "11 wins & 17 nominations."                                           
 [586] "Won 1 Golden Globe. Another 4 wins & 22 nominations."                
 [587] "21 wins & 19 nominations."                                           
 [588] "Nominated for 2 Oscars. Another 5 wins & 9 nominations."             
 [589] "Nominated for 1 Oscar. Another 4 wins & 4 nominations."              
 [590] "13 wins & 1 nomination."                                             
 [591] "Nominated for 3 Golden Globes. Another 12 wins & 14 nominations."    
 [592] "Won 1 Oscar. Another 33 wins & 74 nominations."                      
 [593] "Won 1 Oscar. Another 23 wins & 39 nominations."                      
 [594] "Nominated for 1 Oscar. Another 5 wins & 9 nominations."              
 [595] "41 wins & 16 nominations."                                           
 [596] "Nominated for 3 Oscars. Another 20 wins & 60 nominations."           
 [597] "Nominated for 1 Golden Globe. Another 3 wins & 14 nominations."      
 [598] "Nominated for 2 Oscars. Another 1 win & 13 nominations."             
 [599] "15 wins."                                                            
 [600] "Won 1 Oscar. Another 11 wins & 25 nominations."                      
 [601] "Won 1 BAFTA Film Award. Another 3 wins & 2 nominations."             
 [602] "8 wins & 19 nominations."                                            
 [603] "Nominated for 1 Oscar. Another 3 wins & 17 nominations."             
 [604] "Won 2 Oscars. Another 11 wins & 27 nominations."                     
 [605] "Nominated for 1 Oscar. Another 20 wins & 28 nominations."            
 [606] "Nominated for 1 BAFTA Film Award. Another 2 wins & 4 nominations."   
 [607] "Nominated for 1 Oscar. Another 10 wins & 11 nominations."            
 [608] "24 wins & 11 nominations."                                           
 [609] "15 wins & 12 nominations."                                           
 [610] "Won 1 Oscar. Another 10 wins & 3 nominations."                       
 [611] "2 wins & 13 nominations."                                            
 [612] "Nominated for 1 Oscar. Another 19 wins & 3 nominations."             
 [613] "Won 1 BAFTA Film Award. Another 1 win & 1 nomination."               
 [614] "11 wins & 3 nominations."                                            
 [615] "24 wins & 4 nominations."                                            
 [616] "Nominated for 1 Oscar. Another 4 wins & 13 nominations."             
 [617] "Won 1 Oscar. Another 46 wins & 37 nominations."                      
 [618] "Nominated for 1 BAFTA Film Award. Another 6 wins & 13 nominations."  
 [619] "Nominated for 4 Oscars. Another 14 wins & 35 nominations."           
 [620] "Nominated for 1 Oscar. Another 1 win & 15 nominations."              
 [621] "Nominated for 4 Oscars. Another 3 wins & 9 nominations."             
 [622] "Nominated for 1 Oscar. Another 3 wins & 23 nominations."             
 [623] "23 wins & 5 nominations."                                            
 [624] "15 wins & 19 nominations."                                           
 [625] "Won 1 Oscar. Another 3 nominations."                                 
 [626] "Nominated for 1 Oscar. Another 1 win & 17 nominations."              
 [627] "Nominated for 1 Golden Globe. Another 10 wins & 11 nominations."     
 [628] "Nominated for 5 Oscars. Another 3 wins & 3 nominations."             
 [629] "Won 1 Oscar. Another 34 wins & 53 nominations."                      
 [630] "Nominated for 3 Oscars. Another 11 wins & 47 nominations."           
 [631] "6 wins & 15 nominations."                                            
 [632] "Nominated for 1 Primetime Emmy. Another 3 wins & 1 nomination."      
 [633] "Nominated for 1 Oscar. Another 64 wins & 169 nominations."           
 [634] "Won 1 Oscar. Another 37 wins & 32 nominations."                      
 [635] "5 wins & 28 nominations."                                            
 [636] "Nominated for 4 Oscars. Another 15 wins & 30 nominations."           
 [637] "Nominated for 1 Golden Globe. Another 12 wins & 23 nominations."     
 [638] "Nominated for 4 Oscars. Another 6 wins & 2 nominations."             
 [639] "Nominated for 2 Oscars. Another 4 wins & 22 nominations."            
 [640] "Nominated for 1 Oscar. Another 9 wins & 4 nominations."              
 [641] "Nominated for 1 Oscar. Another 2 wins & 29 nominations."             
 [642] "Nominated for 1 BAFTA Film Award. Another 13 wins & 7 nominations."  
 [643] "14 wins & 3 nominations."                                            
 [644] "16 wins & 16 nominations."                                           
 [645] "Nominated for 1 Oscar. Another 1 win & 3 nominations."               
 [646] "Nominated for 1 Oscar. Another 17 wins & 20 nominations."            
 [647] "13 wins & 10 nominations."                                           
 [648] "Nominated for 1 Oscar. Another 7 nominations."                       
 [649] "Nominated for 2 Oscars. Another 13 wins & 12 nominations."           
 [650] "15 wins & 28 nominations."                                           
 [651] "Nominated for 1 Oscar. Another 4 wins & 1 nomination."               
 [652] "Nominated for 1 Oscar. Another 1 win & 7 nominations."               
 [653] "17 wins & 17 nominations."                                           
 [654] "Nominated for 1 Primetime Emmy. Another 1 nomination."               
 [655] "15 wins & 27 nominations."                                           
 [656] "8 wins & 14 nominations."                                            
 [657] "Nominated for 2 Oscars. Another 10 wins & 28 nominations."           
 [658] "Nominated for 4 Oscars. Another 5 wins & 9 nominations."             
 [659] "Nominated for 1 Oscar. Another 11 wins & 1 nomination."              
 [660] "Nominated for 1 Golden Globe. Another 15 wins & 15 nominations."     
 [661] "Won 1 Oscar. Another 11 wins & 14 nominations."                      
 [662] "Won 5 Oscars. Another 74 wins & 74 nominations."                     
 [663] "Won 7 Oscars. Another 56 wins & 86 nominations."                     
 [664] "11 wins & 8 nominations."                                            
 [665] "Nominated for 1 Golden Globe. Another 8 wins & 6 nominations."       
 [666] "Nominated for 2 Golden Globes. Another 2 wins & 3 nominations."      
 [667] "Nominated for 1 Oscar. Another 13 wins & 26 nominations."            
 [668] "Nominated for 2 Golden Globes. Another 17 wins & 15 nominations."    
 [669] "27 wins & 13 nominations."                                           
 [670] "Nominated for 1 Oscar. Another 4 wins & 9 nominations."              
 [671] "Won 1 Oscar. Another 6 wins & 2 nominations."                        
 [672] "9 wins & 12 nominations."                                            
 [673] "Nominated for 1 Golden Globe. Another 1 win & 1 nomination."         
 [674] "Nominated for 1 Oscar. Another 7 wins & 10 nominations."             
 [675] "Won 1 Oscar. Another 8 wins & 19 nominations."                       
 [676] "Nominated for 1 Oscar. Another 8 wins & 2 nominations."              
 [677] "Nominated for 2 Oscars. Another 7 wins & 10 nominations."            
 [678] "Nominated for 4 Oscars. Another 9 wins & 38 nominations."            
 [679] "Nominated for 2 Oscars. Another 10 wins & 21 nominations."           
 [680] "Won 1 Oscar. Another 29 wins & 51 nominations."                      
 [681] "Won 2 Oscars. Another 35 wins & 49 nominations."                     
 [682] "Nominated for 3 Oscars. Another 29 wins & 49 nominations."           
 [683] "Nominated for 1 Golden Globe. Another 5 wins & 15 nominations."      
 [684] "Nominated for 2 Oscars. Another 7 wins & 9 nominations."             
 [685] "Nominated for 1 Oscar. Another 15 wins & 26 nominations."            
 [686] "16 wins & 3 nominations."                                            
 [687] "10 wins & 8 nominations."                                            
 [688] "Nominated for 1 Oscar. Another 2 wins & 14 nominations."             
 [689] "Nominated for 1 BAFTA Film Award. Another 11 wins."                  
 [690] "Nominated for 1 BAFTA Film Award. Another 5 wins & 1 nomination."    
 [691] "Nominated for 1 Oscar. Another 6 wins & 14 nominations."             
 [692] "Won 1 Oscar. Another 8 wins & 1 nomination."                         
 [693] "Won 2 Oscars. Another 20 wins & 53 nominations."                     
 [694] "Nominated for 1 Oscar. Another 9 wins & 15 nominations."             
 [695] "Nominated for 3 Golden Globes. Another 1 nomination."                
 [696] "Nominated for 1 Oscar. Another 7 wins & 16 nominations."             
 [697] "14 wins & 11 nominations."                                           
 [698] "Nominated for 1 Oscar. Another 7 wins & 21 nominations."             
 [699] "12 wins & 11 nominations."                                           
 [700] "Nominated for 4 Oscars. Another 6 wins & 8 nominations."             
 [701] "Won 2 Oscars. Another 84 wins & 77 nominations."                     
 [702] "Nominated for 1 Golden Globe. Another 4 wins & 5 nominations."       
 [703] "5 wins & 11 nominations."                                            
 [704] "22 wins & 4 nominations."                                            
 [705] "Nominated for 1 Primetime Emmy. Another 5 wins & 2 nominations."     
 [706] "Nominated for 1 Oscar. Another 2 wins & 7 nominations."              
 [707] "Nominated for 1 BAFTA Film Award. Another 2 nominations."            
 [708] "Won 1 Golden Globe. Another 12 wins & 7 nominations."                
 [709] "Won 4 Oscars. Another 155 wins & 131 nominations."                   
 [710] "Won 1 Oscar. Another 18 wins & 39 nominations."                      
 [711] "Won 1 BAFTA Film Award. Another 8 wins & 8 nominations."             
 [712] "Nominated for 1 Oscar. Another 10 wins & 7 nominations."             
 [713] "14 wins & 4 nominations."                                            
 [714] "Nominated for 1 Golden Globe. Another 8 wins & 5 nominations."       
 [715] "Nominated for 1 Golden Globe. Another 3 wins & 7 nominations."       
 [716] "Nominated for 3 Primetime Emmys. Another 4 nominations."             
 [717] "Nominated for 1 BAFTA Film Award. Another 5 wins & 2 nominations."   
 [718] "Won 11 Oscars. Another 110 wins & 73 nominations."                   
 [719] "Nominated for 1 Golden Globe. Another 6 wins & 9 nominations."       
 [720] "Nominated for 2 Primetime Emmys. Another 2 nominations."             
 [721] "Nominated for 1 Oscar. Another 8 wins & 15 nominations."             
 [722] "Nominated for 1 BAFTA Film Award. Another 23 wins & 93 nominations." 
 [723] "Won 1 Oscar. Another 8 wins & 2 nominations."                        
 [724] "Nominated for 1 Oscar. Another 4 wins & 3 nominations."              
 [725] "Nominated for 2 Oscars. Another 2 wins & 17 nominations."            
 [726] "Nominated for 2 Oscars. Another 3 wins & 20 nominations."            
 [727] "Nominated for 1 Oscar. Another 3 nominations."                       
 [728] "Nominated for 1 Golden Globe. Another 4 wins & 7 nominations."       
 [729] "Won 1 BAFTA Film Award. Another 2 nominations."                      
 [730] "Won 1 Primetime Emmy. Another 5 wins & 16 nominations."              
 [731] "Nominated for 1 Golden Globe. Another 10 wins & 18 nominations."     
 [732] "9 wins & 17 nominations."                                            
 [733] "Nominated for 1 BAFTA Film Award. Another 10 wins & 6 nominations."  
 [734] "Nominated for 1 Oscar. Another 42 wins & 25 nominations."            
 [735] "Won 1 BAFTA Film Award. Another 8 wins & 3 nominations."             
 [736] "15 wins & 25 nominations."                                           
 [737] "Nominated for 1 Golden Globe. Another 11 wins & 11 nominations."     
 [738] "Won 1 Oscar. Another 17 wins & 38 nominations."                      
 [739] "12 wins & 3 nominations."                                            
 [740] "Won 2 Oscars. Another 73 wins & 52 nominations."                     
 [741] "Nominated for 1 Golden Globe. Another 5 wins & 14 nominations."      
 [742] "Nominated for 1 Oscar. Another 4 wins & 5 nominations."              
 [743] "Nominated for 2 Oscars. Another 2 wins & 7 nominations."             
 [744] "Nominated for 4 Oscars. Another 9 wins & 20 nominations."            
 [745] "Won 1 Oscar. Another 32 wins & 33 nominations."                      
 [746] "Nominated for 1 Oscar. Another 4 wins & 8 nominations."              
 [747] "Nominated for 1 Oscar. Another 9 wins & 14 nominations."             
 [748] "Won 1 Oscar. Another 25 wins & 37 nominations."                      
 [749] "Won 1 Oscar. Another 20 wins & 12 nominations."                      
 [750] "Nominated for 1 Golden Globe. Another 14 wins & 10 nominations."     
 [751] "Nominated for 1 Oscar. Another 15 wins & 16 nominations."            
 [752] "Nominated for 1 Oscar. Another 5 wins & 4 nominations."              
 [753] "Nominated for 1 Golden Globe. Another 7 wins & 5 nominations."       
 [754] "Nominated for 1 Oscar. Another 4 wins & 12 nominations."             
 [755] "Nominated for 2 Oscars. Another 4 wins & 15 nominations."            
 [756] "Won 2 Oscars. Another 3 wins & 11 nominations."                      
 [757] "Nominated for 1 Oscar. Another 10 wins & 10 nominations."            
 [758] "Nominated for 1 Golden Globe. Another 3 wins & 4 nominations."       
 [759] "Nominated for 1 Oscar. Another 21 wins & 14 nominations."            
 [760] "Nominated for 1 Oscar. Another 14 wins & 26 nominations."            
 [761] "Nominated for 5 Oscars. Another 33 wins & 36 nominations."           
 [762] "7 wins & 18 nominations."                                            
 [763] "Won 1 Oscar. Another 44 wins & 48 nominations."                      
 [764] "Nominated for 1 BAFTA Film Award. Another 6 nominations."            
 [765] "Won 1 Oscar. Another 13 wins & 11 nominations."                      
 [766] "Nominated for 1 Oscar. Another 3 wins & 5 nominations."              
 [767] "Nominated for 1 Oscar. Another 20 wins & 26 nominations."            
 [768] "Nominated for 2 Oscars. Another 10 wins & 13 nominations."           
 [769] "Nominated for 1 Oscar. Another 3 wins & 2 nominations."              
 [770] "Won 1 Oscar. Another 9 wins & 4 nominations."                        
 [771] "Won 2 Oscars. Another 24 wins & 49 nominations."                     
 [772] "Won 1 Oscar. Another 18 wins & 23 nominations."                      
 [773] "Nominated for 3 Oscars. Another 9 wins & 21 nominations."            
 [774] "Won 5 Oscars. Another 26 wins & 29 nominations."                     
 [775] "Nominated for 1 BAFTA Film Award. Another 7 wins & 7 nominations."   
 [776] "Nominated for 1 Oscar. Another 3 wins & 9 nominations."              
 [777] "Nominated for 3 Oscars. Another 5 wins & 6 nominations."             
 [778] "Won 1 Oscar. Another 20 wins & 19 nominations."                      
 [779] "8 wins & 21 nominations."                                            
 [780] "Won 1 Golden Globe. Another 5 wins & 15 nominations."                
 [781] "Nominated for 2 BAFTA Film Awards. Another 2 wins & 6 nominations."  
 [782] "Won 1 Oscar. Another 30 wins & 27 nominations."                      
 [783] "15 wins & 1 nomination."                                             
 [784] "Won 1 Oscar. Another 11 wins & 11 nominations."                      
 [785] "48 wins & 59 nominations."                                           
 [786] "Nominated for 1 BAFTA Film Award. Another 6 wins & 14 nominations."  
 [787] "Nominated for 4 Oscars. Another 10 wins & 13 nominations."           
 [788] "14 wins & 15 nominations."                                           
 [789] "Won 1 Oscar. Another 6 wins & 3 nominations."                        
 [790] "Won 2 Oscars. Another 13 wins & 6 nominations."                      
 [791] "Won 2 Oscars. Another 2 nominations."                                
 [792] "Nominated for 2 Oscars. Another 6 wins & 10 nominations."            
 [793] "Won 1 Oscar. Another 5 wins & 8 nominations."                        
 [794] "Nominated for 1 Oscar. Another 25 wins & 34 nominations."            
 [795] "Won 1 Oscar. Another 31 wins & 45 nominations."                      
 [796] "14 wins & 5 nominations."                                            
 [797] "Won 1 Golden Globe. Another 6 wins & 12 nominations."                
 [798] "Nominated for 2 Oscars. Another 9 wins & 18 nominations."            
 [799] "Nominated for 1 Oscar. Another 5 wins & 8 nominations."              
 [800] "Nominated for 1 BAFTA Film Award. Another 1 win & 6 nominations."    
 [801] "Nominated for 1 Oscar. Another 6 wins."                              
 [802] "Nominated for 1 Oscar. Another 19 wins & 2 nominations."             
 [803] "Won 1 Oscar. Another 3 wins & 5 nominations."                        
 [804] "16 wins & 2 nominations."                                            
 [805] "Won 1 Oscar. Another 18 wins & 47 nominations."                      
 [806] "Won 1 Oscar. Another 18 wins & 15 nominations."                      
 [807] "8 wins & 18 nominations."                                            
 [808] "Nominated for 2 Oscars. Another 3 wins & 6 nominations."             
 [809] "Nominated for 1 Golden Globe. Another 2 wins & 10 nominations."      
 [810] "Nominated for 1 Golden Globe. Another 2 wins & 2 nominations."       
 [811] "Nominated for 1 Oscar. Another 5 wins & 2 nominations."              
 [812] "Won 2 Oscars. Another 22 wins & 15 nominations."                     
 [813] "Won 6 Oscars. Another 37 wins & 51 nominations."                     
 [814] "Nominated for 2 Oscars. Another 23 wins & 20 nominations."           
 [815] "Nominated for 1 Oscar. Another 17 wins & 13 nominations."            
 [816] "Nominated for 1 Oscar. Another 21 wins & 7 nominations."             
 [817] "Nominated for 2 Oscars. Another 18 wins & 21 nominations."           
 [818] "19 wins & 13 nominations."                                           
 [819] "Won 1 Oscar. Another 1 win & 8 nominations."                         
 [820] "Nominated for 3 Oscars. Another 5 wins & 14 nominations."            
 [821] "4 wins & 29 nominations."                                            
 [822] "Won 1 Oscar. Another 1 nomination."                                  
 [823] "Won 3 Oscars. Another 5 wins & 1 nomination."                        
 [824] "Nominated for 1 Golden Globe. Another 8 wins & 10 nominations."      
 [825] "Nominated for 1 Oscar. Another 5 wins & 5 nominations."              
 [826] "Nominated for 2 Oscars. Another 5 wins & 6 nominations."             
 [827] "21 wins & 9 nominations."                                            
 [828] "22 wins & 6 nominations."                                            
 [829] "Won 1 Oscar. Another 54 wins & 60 nominations."                      
 [830] "Nominated for 4 Oscars. Another 5 wins & 24 nominations."            
 [831] "14 wins & 1 nomination."                                             
 [832] "Nominated for 2 Oscars. Another 3 wins & 3 nominations."             
 [833] "Won 1 Oscar. Another 4 wins."                                        
 [834] "Nominated for 1 Oscar. Another 7 wins & 15 nominations."             
 [835] "Won 2 Oscars. Another 17 wins & 22 nominations."                     
 [836] "Nominated for 1 Oscar. Another 4 nominations."                       
 [837] "Won 1 Oscar. Another 2 wins & 3 nominations."                        
 [838] "Won 1 Oscar. Another 3 wins & 10 nominations."                       
 [839] "9 wins & 11 nominations."                                            
 [840] "Won 3 Golden Globes. Another 7 wins & 9 nominations."                
 [841] "Nominated for 2 Golden Globes. Another 1 win & 2 nominations."       
 [842] "Nominated for 3 Oscars. Another 1 win & 7 nominations."              
 [843] "Nominated for 1 Oscar. Another 7 wins & 1 nomination."               
 [844] "Won 1 Oscar. Another 1 win."                                         
 [845] "Won 1 BAFTA Film Award. Another 5 wins & 8 nominations."             
 [846] "Won 1 Golden Globe. Another 2 nominations."                          
 [847] "Nominated for 7 Oscars. Another 7 wins & 21 nominations."            
 [848] "Nominated for 1 Golden Globe. Another 1 nomination."                 
 [849] "Won 3 Oscars. Another 28 wins & 17 nominations."                     
 [850] "Won 1 Oscar. Another 9 wins & 6 nominations."                        
 [851] "Nominated for 1 Golden Globe. Another 5 wins & 8 nominations."       
 [852] "Won 2 Oscars. Another 10 wins & 16 nominations."                     
 [853] "18 wins & 5 nominations."                                            
 [854] "Won 7 Oscars. Another 71 wins & 33 nominations."                     
 [855] "Nominated for 1 Oscar. Another 17 wins & 16 nominations."            
 [856] "Nominated for 2 Oscars. Another 4 wins & 12 nominations."            
 [857] "Nominated for 1 Oscar. Another 5 wins & 3 nominations."              
 [858] "Nominated for 2 Oscars. Another 3 wins & 11 nominations."            
 [859] "Won 2 Oscars. Another 25 wins & 18 nominations."                     
 [860] "Nominated for 2 Oscars. Another 5 wins & 18 nominations."            
 [861] "Nominated for 2 Oscars. Another 2 wins & 15 nominations."            
 [862] "Won 1 Oscar. Another 24 wins & 14 nominations."                      
 [863] "Nominated for 1 Golden Globe. Another 4 wins & 2 nominations."       
 [864] "Nominated for 3 Oscars. Another 3 wins & 11 nominations."            
 [865] "Nominated for 1 Oscar. Another 6 wins & 4 nominations."              
 [866] "Won 1 Oscar. Another 3 wins & 12 nominations."                       
 [867] "Nominated for 3 Oscars. Another 4 wins & 5 nominations."             
 [868] "Nominated for 1 Oscar. Another 5 wins & 7 nominations."              
 [869] "Nominated for 2 Oscars. Another 2 wins & 4 nominations."             
 [870] "Nominated for 2 Golden Globes. Another 1 nomination."                
 [871] "Won 3 Oscars. Another 26 wins & 36 nominations."                     
 [872] "Nominated for 2 Oscars. Another 11 wins & 9 nominations."            
 [873] "Nominated for 2 Oscars. Another 5 nominations."                      
 [874] "Nominated for 2 Oscars. Another 18 wins & 14 nominations."           
 [875] "Nominated for 1 Oscar. Another 6 nominations."                       
 [876] "Won 1 Oscar. Another 3 wins & 2 nominations."                        
 [877] "Nominated for 2 Oscars. Another 15 wins & 7 nominations."            
 [878] "Nominated for 2 Oscars. Another 2 wins & 8 nominations."             
 [879] "Won 1 Oscar. Another 5 wins & 12 nominations."                       
 [880] "Nominated for 1 Oscar. Another 7 wins & 3 nominations."              
 [881] "Nominated for 1 Golden Globe. Another 19 wins & 11 nominations."     
 [882] "Nominated for 2 Oscars. Another 10 nominations."                     
 [883] "Nominated for 1 BAFTA Film Award. Another 15 wins & 11 nominations." 
 [884] "Nominated for 2 Oscars. Another 3 wins & 1 nomination."              
 [885] "Won 4 Oscars. Another 36 wins & 29 nominations."                     
 [886] "Nominated for 2 Golden Globes. Another 2 nominations."               
 [887] "Won 1 Oscar. Another 3 wins & 7 nominations."                        
 [888] "Nominated for 2 Golden Globes. Another 2 wins."                      
 [889] "Won 1 Oscar. Another 9 wins & 3 nominations."                        
 [890] "Nominated for 3 Oscars. Another 1 win & 3 nominations."              
 [891] "Nominated for 3 Oscars. Another 13 wins & 12 nominations."           
 [892] "Won 2 Oscars. Another 25 wins & 21 nominations."                     
 [893] "Won 2 Oscars. Another 10 wins & 31 nominations."                     
 [894] "Nominated for 2 Oscars. Another 4 wins & 14 nominations."            
 [895] "Won 1 Oscar. Another 7 wins & 6 nominations."                        
 [896] "Nominated for 1 BAFTA Film Award. Another 13 wins & 11 nominations." 
 [897] "16 wins & 4 nominations."                                            
 [898] "Nominated for 2 Oscars. Another 6 wins & 8 nominations."             
 [899] "Won 2 Primetime Emmys. Another 5 wins & 5 nominations."              
 [900] "16 wins & 12 nominations."                                           
 [901] "Nominated for 5 Oscars. Another 5 wins & 12 nominations."            
 [902] "Won 2 Oscars. Another 16 wins & 25 nominations."                     
 [903] "Won 1 Oscar. Another 5 wins."                                        
 [904] "Won 1 Oscar. Another 7 wins & 15 nominations."                       
 [905] "14 wins & 8 nominations."                                            
 [906] "25 wins & 8 nominations."                                            
 [907] "Nominated for 1 BAFTA Film Award. Another 3 wins & 2 nominations."   
 [908] "Nominated for 2 Oscars. Another 5 wins & 12 nominations."            
 [909] "Nominated for 1 Oscar. Another 16 wins & 17 nominations."            
 [910] "Nominated for 2 Golden Globes. Another 6 wins & 54 nominations."     
 [911] "Nominated for 2 Oscars. Another 1 win & 5 nominations."              
 [912] "Won 3 Oscars. Another 15 wins & 18 nominations."                     
 [913] "Won 4 Oscars. Another 20 wins & 22 nominations."                     
 [914] "Won 1 Oscar. Another 16 wins & 38 nominations."                      
 [915] "Nominated for 1 Golden Globe. Another 9 wins & 6 nominations."       
 [916] "Won 1 BAFTA Film Award. Another 15 wins & 7 nominations."            
 [917] "12 wins & 19 nominations."                                           
 [918] "Nominated for 3 Oscars. Another 6 wins & 8 nominations."             
 [919] "Won 1 Oscar. Another 30 wins & 23 nominations."                      
 [920] "Won 7 Oscars. Another 43 wins & 32 nominations."                     
 [921] "Won 3 Oscars. Another 6 wins & 30 nominations."                      
 [922] "Nominated for 1 Oscar. Another 6 wins & 15 nominations."             
 [923] "Nominated for 1 Oscar. Another 8 wins & 5 nominations."              
 [924] "Won 2 Oscars. Another 16 wins & 22 nominations."                     
 [925] "Nominated for 2 Oscars. Another 10 wins & 4 nominations."            
 [926] "Nominated for 1 Golden Globe. Another 1 win & 5 nominations."        
 [927] "24 wins & 14 nominations."                                           
 [928] "Nominated for 1 BAFTA Film Award. Another 1 win & 3 nominations."    
 [929] "Nominated for 1 Golden Globe. Another 4 wins."                       
 [930] "Won 1 Oscar. Another 3 wins & 8 nominations."                        
 [931] "Nominated for 1 Oscar. Another 6 wins & 8 nominations."              
 [932] "Nominated for 2 Oscars. Another 2 wins & 9 nominations."             
 [933] "Won 1 Oscar. Another 11 wins & 15 nominations."                      
 [934] "14 wins."                                                            
 [935] "Nominated for 2 Oscars. Another 7 wins & 13 nominations."            
 [936] "Nominated for 1 Golden Globe. Another 1 win."                        
 [937] "Nominated for 1 Oscar. Another 3 wins & 6 nominations."              
 [938] "Nominated for 1 Oscar. Another 11 wins & 33 nominations."            
 [939] "Nominated for 1 Oscar. Another 9 wins & 7 nominations."              
 [940] "Won 1 Oscar. Another 9 wins & 22 nominations."                       
 [941] "Nominated for 2 Oscars. Another 2 wins & 1 nomination."              
 [942] "Won 2 Oscars. Another 13 wins & 23 nominations."                     
 [943] "Nominated for 3 Oscars. Another 13 wins & 19 nominations."           
 [944] "Won 1 Oscar. Another 17 wins & 18 nominations."                      
 [945] "Nominated for 2 Oscars. Another 15 wins & 11 nominations."           
 [946] "Won 4 Oscars. Another 17 wins & 21 nominations."                     
 [947] "Nominated for 3 Oscars. Another 4 wins & 6 nominations."             
 [948] "Nominated for 1 Golden Globe. Another 31 wins & 34 nominations."     
 [949] "Won 3 Oscars. Another 11 wins & 16 nominations."                     
 [950] "Won 1 Oscar. Another 9 wins & 13 nominations."                       
 [951] "Won 1 BAFTA Film Award. Another 1 win & 10 nominations."             
 [952] "Won 1 Oscar. Another 5 wins & 20 nominations."                       
 [953] "8 wins & 59 nominations."                                            
 [954] "Nominated for 1 Oscar. Another 31 wins & 40 nominations."            
 [955] "32 wins & 85 nominations."                                           
 [956] "Won 2 Oscars. Another 20 wins & 19 nominations."                     
 [957] "Nominated for 2 Oscars. Another 4 wins & 10 nominations."            
 [958] "Nominated for 1 Oscar. Another 14 wins & 20 nominations."            
 [959] "Nominated for 2 Oscars. Another 4 wins & 7 nominations."             
 [960] "Nominated for 1 Oscar. Another 4 wins & 15 nominations."             
 [961] "Won 2 Oscars. Another 32 wins & 62 nominations."                     
 [962] "Nominated for 2 Oscars. Another 11 wins & 12 nominations."           
 [963] "Won 1 Oscar. Another 8 wins & 9 nominations."                        
 [964] "Nominated for 1 Oscar. Another 7 wins & 4 nominations."              
 [965] "Nominated for 1 Golden Globe. Another 6 wins & 4 nominations."       
 [966] "Nominated for 2 Oscars. Another 5 wins & 2 nominations."             
 [967] "Won 3 Oscars. Another 16 wins & 20 nominations."                     
 [968] "20 wins & 13 nominations."                                           
 [969] "Nominated for 3 Golden Globes. Another 19 wins & 39 nominations."    
 [970] "Won 1 Golden Globe. Another 1 win & 6 nominations."                  
 [971] "Nominated for 2 Golden Globes. Another 2 wins & 1 nomination."       
 [972] "Won 1 Oscar. Another 16 wins & 24 nominations."                      
 [973] "Won 4 Oscars. Another 22 wins & 21 nominations."                     
 [974] "Won 1 Oscar. Another 1 win & 1 nomination."                          
 [975] "8 wins & 22 nominations."                                            
 [976] "Nominated for 1 Oscar. Another 12 wins & 7 nominations."             
 [977] "Won 1 Golden Globe. Another 2 wins & 15 nominations."                
 [978] "47 wins & 50 nominations."                                           
 [979] "Nominated for 1 Oscar. Another 11 wins & 6 nominations."             
 [980] "18 wins & 40 nominations."                                           
 [981] "Nominated for 3 Oscars. Another 8 wins & 6 nominations."             
 [982] "Won 3 Oscars. Another 19 wins & 21 nominations."                     
 [983] "Nominated for 2 Oscars. Another 1 win & 9 nominations."              
 [984] "Nominated for 1 Oscar. Another 5 wins & 49 nominations."             
 [985] "Won 1 Oscar. Another 8 wins & 16 nominations."                       
 [986] "Nominated for 1 Golden Globe. Another 2 wins & 1 nomination."        
 [987] "Won 1 BAFTA Film Award. Another 2 wins & 3 nominations."             
 [988] "Nominated for 2 Oscars. Another 27 wins & 10 nominations."           
 [989] "Nominated for 2 Golden Globes. Another 3 nominations."               
 [990] "Nominated for 7 Oscars. Another 13 wins & 16 nominations."           
 [991] "Nominated for 3 Oscars. Another 5 wins & 11 nominations."            
 [992] "Nominated for 6 Oscars. Another 12 wins & 10 nominations."           
 [993] "Nominated for 6 Oscars. Another 7 wins & 16 nominations."            
 [994] "10 wins & 38 nominations."                                           
 [995] "Won 1 Oscar. Another 1 win & 7 nominations."                         
 [996] "Nominated for 5 Oscars. Another 16 wins & 24 nominations."           
 [997] "Nominated for 1 Golden Globe. Another 6 wins & 3 nominations."       
 [998] "Won 1 Oscar. Another 4 nominations."                                 
 [999] "Nominated for 2 Oscars. Another 2 wins & 2 nominations."             
[1000] "Nominated for 1 Golden Globe. Another 2 wins."                       
[1001] "Nominated for 2 BAFTA Film Awards. Another 1 win."                   
[1002] "Nominated for 2 Oscars. Another 3 wins & 7 nominations."             
[1003] "Nominated for 1 Golden Globe. Another 3 wins & 1 nomination."        
[1004] "Nominated for 1 Golden Globe. Another 48 wins & 88 nominations."     
[1005] "Nominated for 1 Oscar. Another 2 wins & 3 nominations."              
[1006] "16 wins & 1 nomination."                                             
[1007] "10 wins & 23 nominations."                                           
[1008] "Won 1 Oscar. Another 9 wins & 11 nominations."                       
[1009] "Nominated for 1 Oscar. Another 18 wins & 16 nominations."            
[1010] "Nominated for 2 Golden Globes. Another 7 wins & 19 nominations."     
[1011] "Won 1 Oscar. Another 4 wins & 12 nominations."                       
[1012] "Nominated for 3 Oscars. Another 3 wins & 1 nomination."              
[1013] "Nominated for 1 Oscar. Another 3 wins & 7 nominations."              
[1014] "Nominated for 2 Golden Globes. Another 1 win & 3 nominations."       
[1015] "Won 1 Golden Globe. Another 2 wins & 5 nominations."                 
[1016] "Won 3 Oscars. Another 21 wins & 26 nominations."                     
[1017] "Won 3 Oscars. Another 12 wins & 17 nominations."                     
[1018] "Nominated for 1 Golden Globe. Another 11 wins & 16 nominations."     
[1019] "Nominated for 1 BAFTA Film Award. Another 3 nominations."            
[1020] "Nominated for 2 Oscars. Another 2 wins & 11 nominations."            
[1021] "Won 2 Oscars. Another 68 wins & 104 nominations."                    
[1022] "Nominated for 1 Oscar. Another 13 wins & 13 nominations."            
[1023] "Nominated for 3 Oscars. Another 3 wins & 9 nominations."             
[1024] "Won 4 Oscars. Another 19 wins & 14 nominations."                     
[1025] "Won 1 Oscar. Another 2 wins & 1 nomination."                         
[1026] "Nominated for 1 BAFTA Film Award. Another 4 wins & 1 nomination."    
[1027] "Nominated for 3 Golden Globes. Another 2 wins & 3 nominations."      
[1028] "Nominated for 4 Oscars. Another 3 wins & 13 nominations."            
[1029] "Won 1 Oscar. Another 10 wins & 5 nominations."                       
[1030] "Won 2 Primetime Emmys. Another 1 nomination."                        
[1031] "Nominated for 1 Golden Globe. Another 5 wins & 6 nominations."       
[1032] "Nominated for 1 Golden Globe. Another 3 wins & 8 nominations."       
[1033] "Nominated for 3 Oscars. Another 1 win & 2 nominations."              
[1034] "Won 1 Oscar. Another 18 wins & 26 nominations."                      
[1035] "Nominated for 2 Oscars. Another 8 wins & 3 nominations."             
[1036] "Nominated for 2 BAFTA Film Awards. Another 6 wins & 5 nominations."  
[1037] "Won 1 Oscar. Another 10 wins & 11 nominations."                      
[1038] "Nominated for 2 Oscars. Another 4 wins & 5 nominations."             
[1039] "Nominated for 1 BAFTA Film Award. Another 2 wins."                   
[1040] "Nominated for 1 Golden Globe. Another 1 win & 7 nominations."        
[1041] "Won 1 Oscar. Another 2 wins & 6 nominations."                        
[1042] "Nominated for 2 Oscars. Another 2 nominations."                      
[1043] "Nominated for 1 Oscar. Another 5 wins & 6 nominations."              
[1044] "Won 7 Oscars. Another 22 wins & 28 nominations."                     
[1045] "Nominated for 2 BAFTA Film Awards. Another 2 wins & 3 nominations."  
[1046] "Won 1 Oscar. Another 24 wins & 25 nominations."                      
[1047] "Won 1 Oscar. Another 25 wins & 22 nominations."                      
[1048] "Won 11 Oscars. Another 174 wins & 113 nominations."                  
[1049] "Nominated for 3 Oscars. Another 2 wins & 5 nominations."             
[1050] "Nominated for 1 BAFTA Film Award. Another 3 wins & 11 nominations."  
[1051] "Won 1 Oscar. Another 2 wins & 2 nominations."                        
[1052] "Won 2 Oscars. Another 10 wins & 26 nominations."                     
[1053] "Nominated for 2 Golden Globes. Another 1 win & 7 nominations."       
[1054] "Won 8 Oscars. Another 33 wins & 14 nominations."                     
[1055] "Nominated for 3 BAFTA Film Awards. Another 1 win & 1 nomination."    
[1056] "Nominated for 2 BAFTA Film Awards. Another 2 wins & 1 nomination."   
[1057] "Nominated for 1 Oscar. Another 3 wins & 8 nominations."              
[1058] "Nominated for 2 Oscars. Another 1 win & 4 nominations."              
[1059] "Nominated for 1 Oscar. Another 11 wins & 47 nominations."            
[1060] "Nominated for 3 Oscars. Another 3 wins & 12 nominations."            
[1061] "Won 1 Oscar. Another 6 wins & 20 nominations."                       
[1062] "Won 4 Oscars. Another 16 wins & 14 nominations."                     
[1063] "Nominated for 2 Golden Globes. Another 11 wins & 7 nominations."     
[1064] "Nominated for 1 Golden Globe. Another 15 wins & 8 nominations."      
[1065] "Won 2 Oscars. Another 11 wins & 14 nominations."                     
[1066] "Won 1 Oscar. Another 4 wins & 4 nominations."                        
[1067] "Nominated for 1 Oscar. Another 8 wins & 3 nominations."              
[1068] "Nominated for 1 Oscar. Another 2 wins & 8 nominations."              
[1069] "Nominated for 2 Oscars. Another 3 wins & 5 nominations."             
[1070] "Nominated for 1 BAFTA Film Award. Another 4 wins & 2 nominations."   
[1071] "Nominated for 4 Oscars. Another 2 wins & 3 nominations."             
[1072] "Won 1 BAFTA Film Award. Another 6 wins & 6 nominations."             
[1073] "Nominated for 1 BAFTA Film Award. Another 3 wins & 1 nomination."    
[1074] "Nominated for 3 Oscars. Another 6 wins & 5 nominations."             
[1075] "Won 1 Oscar. Another 10 wins & 14 nominations."                      
[1076] "Won 1 Oscar. Another 4 wins & 1 nomination."                         
[1077] "Won 1 BAFTA Film Award. Another 1 win & 8 nominations."              
[1078] "Won 1 BAFTA Film Award. Another 3 wins & 6 nominations."             
[1079] "Won 1 BAFTA Film Award. Another 8 wins & 6 nominations."             
[1080] "Won 1 Oscar. Another 19 wins & 1 nomination."                        
[1081] "Nominated for 2 Oscars. Another 2 wins & 6 nominations."             
[1082] "Nominated for 1 Oscar. Another 13 wins & 7 nominations."             
[1083] "Nominated for 3 Golden Globes. Another 4 nominations."               
[1084] "Nominated for 5 Oscars. Another 2 wins & 13 nominations."            
[1085] "Nominated for 4 Oscars. Another 16 wins & 16 nominations."           
[1086] "Won 2 Oscars. Another 7 wins & 13 nominations."                      
[1087] "Won 5 Oscars. Another 26 wins & 17 nominations."                     
[1088] "Nominated for 1 Oscar. Another 2 wins & 5 nominations."              
[1089] "Nominated for 3 Oscars. Another 4 wins & 11 nominations."            
[1090] "Nominated for 5 Oscars. Another 43 wins & 106 nominations."          
[1091] "Won 1 Oscar. Another 5 wins & 14 nominations."                       
[1092] "Nominated for 2 Oscars. Another 6 wins & 14 nominations."            
[1093] "Nominated for 2 Golden Globes. Another 12 wins & 26 nominations."    
[1094] "Nominated for 2 Oscars. Another 11 wins & 16 nominations."           
[1095] "6 wins & 19 nominations."                                            
[1096] "Won 1 Golden Globe. Another 1 win & 9 nominations."                  
[1097] "Won 4 Oscars. Another 46 wins & 32 nominations."                     
[1098] "Nominated for 1 Golden Globe. Another 4 wins & 3 nominations."       
[1099] "Won 8 Oscars. Another 27 wins & 20 nominations."                     
[1100] "Won 1 Oscar. Another 9 wins & 17 nominations."                       
[1101] "Won 2 BAFTA Film Awards. Another 1 nomination."                      
[1102] "Nominated for 3 Oscars. Another 4 wins & 4 nominations."             
[1103] "Nominated for 2 Golden Globes. Another 5 nominations."               
[1104] "Won 1 Oscar. Another 12 wins & 11 nominations."                      
[1105] "Won 1 Oscar. Another 24 wins & 29 nominations."                      
[1106] "Nominated for 1 Golden Globe. Another 6 wins & 1 nomination."        
[1107] "Won 1 Oscar. Another 86 wins & 85 nominations."                      
[1108] "Nominated for 3 Oscars. Another 3 wins & 5 nominations."             
[1109] "Won 4 Oscars. Another 14 wins & 19 nominations."                     
[1110] "Nominated for 1 Oscar. Another 2 wins & 10 nominations."             
[1111] "Nominated for 1 BAFTA Film Award. Another 7 wins & 8 nominations."   
[1112] "Nominated for 1 Oscar. Another 2 wins & 9 nominations."              
[1113] "Nominated for 1 Golden Globe. Another 11 wins & 5 nominations."      
[1114] "Won 1 BAFTA Film Award. Another 1 win & 3 nominations."              
[1115] "Nominated for 1 Oscar. Another 8 nominations."                       
[1116] "Won 1 Oscar. Another 12 wins & 4 nominations."                       
[1117] "Won 3 Oscars. Another 11 wins & 23 nominations."                     
[1118] "Nominated for 3 Oscars. Another 3 wins & 6 nominations."             
[1119] "Nominated for 1 Oscar. Another 4 wins & 14 nominations."             
[1120] "Nominated for 1 Golden Globe. Another 4 wins & 4 nominations."       
[1121] "Nominated for 8 Oscars. Another 2 wins & 13 nominations."            
[1122] "Won 3 Oscars. Another 19 wins & 34 nominations."                     
[1123] "Nominated for 1 Oscar. Another 13 wins & 6 nominations."             
[1124] "Nominated for 1 Golden Globe. Another 2 wins & 6 nominations."       
[1125] "Nominated for 5 Oscars. Another 25 wins & 16 nominations."           
[1126] "Won 1 Oscar. Another 8 wins & 13 nominations."                       
[1127] "Nominated for 1 Golden Globe. Another 1 win & 9 nominations."        
[1128] "Won 2 Oscars. Another 4 wins & 17 nominations."                      
[1129] "Nominated for 2 BAFTA Film Awards. Another 2 wins & 9 nominations."  
[1130] "Nominated for 2 Oscars. Another 20 wins & 3 nominations."            
[1131] "7 wins & 10 nominations."                                            
[1132] "Won 1 Primetime Emmy. Another 1 nomination."                         
[1133] "Won 2 Oscars. Another 15 wins & 9 nominations."                      
[1134] "Won 4 Oscars. Another 15 wins & 14 nominations."                     
[1135] "Won 2 Oscars. Another 22 wins & 24 nominations."                     
[1136] "Won 1 Oscar. Another 16 wins & 18 nominations."                      
[1137] "17 wins & 7 nominations."                                            
[1138] "Nominated for 2 Golden Globes. Another 4 nominations."               
[1139] "Nominated for 2 Oscars. Another 1 win & 1 nomination."               
[1140] "Nominated for 2 Oscars. Another 9 nominations."                      
[1141] "Nominated for 3 Oscars. Another 2 nominations."                      
[1142] "Won 1 Oscar. Another 16 wins & 19 nominations."                      
[1143] "Won 4 Oscars. Another 6 wins & 14 nominations."                      
[1144] "Won 2 Oscars. Another 18 wins & 32 nominations."                     
[1145] "Won 1 Primetime Emmy. Another 6 nominations."                        
[1146] "Won 1 Oscar. Another 12 wins & 15 nominations."                      
[1147] "Nominated for 2 BAFTA Film Awards. Another 4 wins & 2 nominations."  
[1148] "Nominated for 2 Golden Globes. Another 2 wins & 2 nominations."      
[1149] "Nominated for 2 Primetime Emmys. Another 5 nominations."             
[1150] "Won 5 Oscars. Another 32 wins & 23 nominations."                     
[1151] "Nominated for 1 Golden Globe. Another 3 wins & 3 nominations."       
[1152] "Nominated for 2 Oscars. Another 15 wins & 21 nominations."           
[1153] "Won 2 Primetime Emmys. Another 1 win & 6 nominations."               
[1154] "Nominated for 1 Oscar. Another 11 wins & 7 nominations."             
[1155] "Won 2 Oscars. Another 10 wins & 6 nominations."                      
[1156] "Nominated for 3 Oscars. Another 3 wins & 16 nominations."            
[1157] "Won 3 Oscars. Another 12 wins & 14 nominations."                     
[1158] "8 wins & 16 nominations."                                            
[1159] "13 wins & 14 nominations."                                           
[1160] "Nominated for 2 Golden Globes. Another 6 wins & 5 nominations."      
[1161] "Won 1 Oscar. Another 4 wins & 10 nominations."                       
[1162] "Won 3 Oscars. Another 11 wins & 14 nominations."                     
[1163] "Won 1 Oscar. Another 12 wins & 12 nominations."                      
[1164] "Won 1 Oscar. Another 4 wins & 5 nominations."                        
[1165] "Nominated for 5 Oscars. Another 9 wins & 11 nominations."            
[1166] "Won 3 Oscars. Another 10 wins & 16 nominations."                     
[1167] "Won 4 Oscars. Another 108 wins & 122 nominations."                   
[1168] "Nominated for 1 Golden Globe. Another 2 wins & 4 nominations."       
[1169] "Won 2 Oscars. Another 14 wins & 14 nominations."                     
[1170] "Nominated for 3 Golden Globes. Another 3 wins & 7 nominations."      
[1171] "Nominated for 4 Oscars. Another 1 win & 3 nominations."              
[1172] "Nominated for 3 Oscars. Another 15 wins & 17 nominations."           
[1173] "Nominated for 1 BAFTA Film Award. Another 3 wins & 4 nominations."   
[1174] "Won 4 Oscars. Another 26 wins & 8 nominations."                      
[1175] "Won 1 Oscar. Another 13 wins & 37 nominations."                      
[1176] "Nominated for 3 Oscars. Another 5 wins & 4 nominations."             
[1177] "Won 3 Oscars. Another 18 wins & 24 nominations."                     
[1178] "Nominated for 2 Oscars. Another 1 win & 3 nominations."              
[1179] "Nominated for 4 Golden Globes. Another 2 wins & 2 nominations."      
[1180] "Nominated for 1 BAFTA Film Award. Another 8 wins."                   
[1181] "Nominated for 2 Oscars. Another 4 nominations."                      
[1182] "Nominated for 1 Oscar. Another 4 wins & 11 nominations."             
[1183] "Nominated for 3 BAFTA Film Awards. Another 1 nomination."            
[1184] "Won 4 Oscars. Another 12 wins & 21 nominations."                     
[1185] "Won 2 Oscars. Another 4 wins & 11 nominations."                      
[1186] "Won 7 Oscars. Another 215 wins & 169 nominations."                   
[1187] "Won 1 Oscar. Another 8 wins."                                        
[1188] "Won 3 Oscars. Another 42 wins & 92 nominations."                     
[1189] "Nominated for 2 Oscars. Another 7 wins & 2 nominations."             
[1190] "Won 4 Oscars. Another 15 wins & 25 nominations."                     
[1191] "Won 3 Oscars. Another 16 wins & 21 nominations."                     
[1192] "Nominated for 4 Golden Globes. Another 1 win & 1 nomination."        
[1193] "Nominated for 4 Oscars. Another 21 wins & 15 nominations."           
[1194] "Nominated for 3 Oscars. Another 1 win & 6 nominations."              
[1195] "Won 4 Oscars. Another 13 wins & 13 nominations."                     
[1196] "Nominated for 2 Oscars. Another 1 win."                              
[1197] "Won 1 Oscar. Another 7 wins."                                        
[1198] "Nominated for 5 Oscars. Another 6 nominations."                      
[1199] "Won 2 Oscars. Another 43 wins & 74 nominations."                     
[1200] "Nominated for 1 Oscar. Another 6 wins & 2 nominations."              
[1201] "Nominated for 1 Oscar. Another 39 wins & 47 nominations."            
[1202] "Won 1 Oscar. Another 22 wins & 25 nominations."                      
[1203] "Won 1 BAFTA Film Award. Another 3 wins & 11 nominations."            
[1204] "Nominated for 1 Oscar. Another 17 wins & 14 nominations."            
[1205] "Won 1 Oscar. Another 5 wins & 11 nominations."                       
[1206] "Nominated for 3 Oscars. Another 3 wins & 2 nominations."             
[1207] "Won 1 Oscar. Another 20 wins & 23 nominations."                      
[1208] "Won 1 Oscar. Another 3 wins & 3 nominations."                        
[1209] "45 wins & 31 nominations."                                           
[1210] "Nominated for 6 Oscars. Another 7 wins & 10 nominations."            
[1211] "Won 1 BAFTA Film Award. Another 2 wins & 1 nomination."              
[1212] "Won 1 Oscar. Another 21 wins & 29 nominations."                      
[1213] "Nominated for 2 Oscars. Another 10 wins & 5 nominations."            
[1214] "Won 1 Primetime Emmy. Another 1 win & 3 nominations."                
[1215] "Won 1 Oscar. Another 17 wins & 8 nominations."                       
[1216] "Nominated for 5 Oscars. Another 9 wins & 8 nominations."             
[1217] "12 wins & 26 nominations."                                           
[1218] "Won 1 BAFTA Film Award. Another 1 nomination."                       
[1219] "Nominated for 3 Primetime Emmys. Another 1 nomination."              
[1220] "Won 1 BAFTA Film Award. Another 8 nominations."                      
[1221] "Nominated for 1 Oscar. Another 3 wins & 11 nominations."             
[1222] "Won 2 Oscars. Another 5 nominations."                                
[1223] "Won 1 Oscar. Another 7 wins & 10 nominations."                       
[1224] "Won 1 Oscar. Another 1 win & 5 nominations."                         
[1225] "Won 1 Golden Globe. Another 6 nominations."                          
[1226] "Won 8 Oscars. Another 27 wins & 15 nominations."                     
[1227] "Nominated for 3 Oscars. Another 2 wins & 10 nominations."            
[1228] "Nominated for 4 Golden Globes. Another 2 wins & 3 nominations."      
[1229] "Nominated for 2 BAFTA Film Awards. Another 1 nomination."            
[1230] "Nominated for 5 Oscars. Another 5 wins & 3 nominations."             
[1231] "Nominated for 4 Oscars. Another 5 wins & 7 nominations."             
[1232] "Nominated for 4 Oscars. Another 4 wins & 8 nominations."             
[1233] "Won 1 Oscar. Another 10 nominations."                                
[1234] "Nominated for 3 Oscars. Another 3 wins & 7 nominations."             
[1235] "Won 3 Oscars. Another 6 wins & 13 nominations."                      
[1236] "Nominated for 1 Golden Globe. Another 3 wins & 2 nominations."       
[1237] "Won 1 Oscar. Another 8 wins & 5 nominations."                        
[1238] "Nominated for 4 Oscars. Another 3 wins & 4 nominations."             
[1239] "Nominated for 5 Oscars. Another 2 wins & 5 nominations."             
[1240] "Won 2 Oscars. Another 1 win & 11 nominations."                       
[1241] "Won 1 Oscar. Another 3 wins & 11 nominations."                       
[1242] "Nominated for 4 Oscars. Another 12 wins & 7 nominations."            
[1243] "Nominated for 6 BAFTA Film Awards. Another 2 wins & 2 nominations."  
[1244] "14 wins & 14 nominations."                                           
[1245] "13 wins & 11 nominations."                                           
[1246] "Won 1 Oscar. Another 3 wins & 19 nominations."                       
[1247] "Nominated for 1 BAFTA Film Award. Another 4 nominations."            
[1248] "Nominated for 3 Oscars. Another 1 win & 4 nominations."              
[1249] "Nominated for 4 Oscars. Another 10 wins & 18 nominations."           
[1250] "Nominated for 3 Oscars. Another 2 wins & 4 nominations."             
[1251] "Won 1 Oscar. Another 6 wins & 7 nominations."                        
[1252] "Won 1 Oscar. Another 9 wins & 16 nominations."                       
[1253] "Won 1 Oscar. Another 8 nominations."                                 
[1254] "Won 1 Oscar. Another 14 wins & 25 nominations."                      
[1255] "23 wins & 37 nominations."                                           
[1256] "Won 7 Oscars. Another 17 wins & 8 nominations."                      
[1257] "Won 2 Oscars. Another 7 wins & 21 nominations."                      
[1258] "Nominated for 4 Oscars. Another 1 win & 6 nominations."              
[1259] "Won 1 Oscar. Another 1 win & 2 nominations."                         
[1260] "Won 1 Oscar. Another 5 wins & 16 nominations."                       
[1261] "Won 1 BAFTA Film Award. Another 5 nominations."                      
[1262] "Nominated for 2 Oscars. Another 8 wins & 12 nominations."            
[1263] "Nominated for 3 Golden Globes. Another 1 win & 2 nominations."       
[1264] "Won 2 BAFTA Film Awards. Another 3 wins & 4 nominations."            
[1265] "Won 1 Golden Globe. Another 4 nominations."                          
[1266] "Won 3 Oscars. Another 24 wins & 15 nominations."                     
[1267] "Won 1 Golden Globe. Another 7 wins & 8 nominations."                 
[1268] "Nominated for 3 Oscars. Another 3 nominations."                      
[1269] "Won 2 BAFTA Film Awards. Another 1 win & 1 nomination."              
[1270] "Won 1 Oscar. Another 9 wins & 23 nominations."                       
[1271] "Nominated for 10 Oscars. Another 37 wins & 148 nominations."         
[1272] "Won 1 Oscar. Another 5 wins & 18 nominations."                       
[1273] "Won 1 Oscar. Another 13 wins & 7 nominations."                       
[1274] "Won 1 Oscar. Another 6 wins & 9 nominations."                        
[1275] "Nominated for 3 Golden Globes. Another 1 win & 1 nomination."        
[1276] "6 wins & 17 nominations."                                            
[1277] "Nominated for 2 Oscars. Another 1 win & 7 nominations."              
[1278] "Won 1 Oscar. Another 6 wins & 16 nominations."                       
[1279] "Won 5 Oscars. Another 7 wins & 25 nominations."                      
[1280] "Nominated for 4 Oscars. Another 7 wins & 5 nominations."             
[1281] "Won 1 Oscar. Another 9 wins & 12 nominations."                       
[1282] "Won 1 Oscar. Another 1 win & 6 nominations."                         
[1283] "Won 6 Oscars. Another 39 wins & 28 nominations."                     
[1284] "Won 2 Oscars. Another 20 wins & 27 nominations."                     
[1285] "Won 1 BAFTA Film Award. Another 25 wins & 39 nominations."           
[1286] "Won 2 Oscars. Another 7 wins & 22 nominations."                      
[1287] "Nominated for 4 Oscars. Another 4 wins & 5 nominations."             
[1288] "Won 5 Oscars. Another 17 wins & 14 nominations."                     
[1289] "Won 1 Oscar. Another 4 wins & 11 nominations."                       
[1290] "Nominated for 1 BAFTA Film Award. Another 1 win & 2 nominations."    
[1291] "Won 1 BAFTA Film Award. Another 1 win & 4 nominations."              
[1292] "Won 2 Oscars. Another 3 wins & 7 nominations."                       
[1293] "Won 2 Oscars. Another 2 wins & 6 nominations."                       
[1294] "Nominated for 4 Oscars. Another 5 wins & 13 nominations."            
[1295] "Won 3 Oscars. Another 4 nominations."                                
[1296] "Won 2 Oscars. Another 14 wins & 15 nominations."                     
[1297] "Won 2 Oscars. Another 4 wins & 5 nominations."                       
[1298] "Nominated for 3 Oscars. Another 102 wins & 111 nominations."         
[1299] "Won 5 Oscars. Another 17 wins & 23 nominations."                     
[1300] "Won 1 Oscar. Another 9 wins & 14 nominations."                       
[1301] "Won 3 Oscars. Another 15 wins & 8 nominations."                      
[1302] "Won 5 Oscars. Another 16 wins & 13 nominations."                     
[1303] "Nominated for 2 BAFTA Film Awards. Another 3 nominations."           
[1304] "Nominated for 3 Oscars. Another 2 wins & 1 nomination."              
[1305] "Won 2 Oscars. Another 3 wins & 13 nominations."                      
[1306] "Won 1 Oscar. Another 13 wins & 23 nominations."                      
[1307] "Nominated for 4 Oscars. Another 13 wins & 6 nominations."            
[1308] "Won 1 Golden Globe. Another 2 wins & 3 nominations."                 
[1309] "Won 1 Golden Globe. Another 1 win & 2 nominations."                  
[1310] "Won 1 Golden Globe. Another 1 win."                                  
[1311] "Nominated for 4 BAFTA Film Awards. Another 2 wins & 2 nominations."  
[1312] "Won 5 Oscars. Another 15 wins & 14 nominations."                     
[1313] "Won 8 Oscars. Another 15 wins & 13 nominations."                     
[1314] "Nominated for 2 Oscars. Another 2 wins."                             
[1315] "Nominated for 2 Oscars. Another 4 wins & 6 nominations."             
[1316] "Won 1 Oscar. Another 3 wins & 4 nominations."                        
[1317] "Nominated for 2 Oscars. Another 3 nominations."                      
[1318] "Nominated for 6 BAFTA Film Awards. Another 1 nomination."            
[1319] "Nominated for 1 Golden Globe. Another 7 wins & 1 nomination."        
[1320] "Nominated for 3 Oscars. Another 7 nominations."                      
[1321] "Won 4 Oscars. Another 2 wins & 13 nominations."                      
[1322] "Nominated for 1 Golden Globe. Another 2 wins & 5 nominations."       
[1323] "Won 1 Oscar. Another 5 wins & 7 nominations."                        
[1324] "5 wins & 15 nominations."                                            
[1325] "Won 1 Oscar. Another 10 wins & 13 nominations."                      
[1326] "Nominated for 5 Oscars. Another 1 win & 6 nominations."              
[1327] "Nominated for 2 Oscars. Another 3 wins & 8 nominations."             
[1328] "Won 4 Oscars. Another 16 wins & 20 nominations."                     
[1329] "Nominated for 2 Oscars. Another 5 wins & 5 nominations."             
[1330] "Nominated for 3 Oscars. Another 1 win & 10 nominations."             
[1331] "Nominated for 5 Golden Globes. Another 1 nomination."                
[1332] "Won 3 Oscars. Another 7 wins & 5 nominations."                       
[1333] "Won 7 Oscars. Another 23 wins & 14 nominations."                     
[1334] "Won 1 Oscar. Another 14 wins & 44 nominations."                      
[1335] "Nominated for 4 BAFTA Film Awards. Another 1 win."                   
[1336] "Nominated for 2 BAFTA Film Awards. Another 1 win & 1 nomination."    
[1337] "Nominated for 3 Oscars. Another 6 wins & 2 nominations."             
[1338] "Nominated for 1 Oscar. Another 7 wins."                              
[1339] "Won 1 Oscar. Another 1 win & 11 nominations."                        
[1340] "Nominated for 1 BAFTA Film Award. Another 2 wins & 2 nominations."   
[1341] "Won 2 Oscars. Another 10 wins & 12 nominations."                     
[1342] "Won 1 Golden Globe. Another 1 win & 1 nomination."                   
[1343] "Nominated for 3 Oscars. Another 6 wins & 11 nominations."            
[1344] "Nominated for 5 Oscars. Another 1 win & 11 nominations."             
[1345] "Nominated for 5 Oscars. Another 1 win & 7 nominations."              
[1346] "Won 2 Oscars. Another 13 wins & 25 nominations."                     
[1347] "Won 1 Oscar. Another 44 wins & 43 nominations."                      
[1348] "Nominated for 3 Primetime Emmys. Another 2 wins & 4 nominations."    
[1349] "Nominated for 4 Oscars. Another 4 wins & 6 nominations."             
[1350] "Won 10 Oscars. Another 18 wins & 11 nominations."                    
[1351] "Won 1 Oscar. Another 6 nominations."                                 
[1352] "Won 3 Oscars. Another 2 wins & 4 nominations."                       
[1353] "Won 3 Oscars. Another 8 wins & 12 nominations."                      
[1354] "Won 1 Oscar. Another 4 wins & 7 nominations."                        
[1355] "Won 1 Golden Globe. Another 2 wins."                                 
[1356] "Nominated for 4 Oscars. Another 3 wins & 7 nominations."             
[1357] "Nominated for 7 Oscars. Another 2 wins & 5 nominations."             
[1358] "Won 3 BAFTA Film Awards. Another 7 wins & 3 nominations."            
[1359] "Won 1 Oscar. Another 1 win & 3 nominations."                         
[1360] "Won 1 Oscar. Another 7 wins & 16 nominations."                       
[1361] "Nominated for 4 Oscars. Another 3 wins & 3 nominations."             
[1362] "Nominated for 7 Oscars. Another 9 wins & 11 nominations."            
[1363] "Won 11 Oscars. Another 16 wins & 13 nominations."                    
[1364] "Nominated for 3 Oscars. Another 1 win & 1 nomination."               
[1365] "Nominated for 1 BAFTA Film Award. Another 1 win & 4 nominations."    
[1366] "Nominated for 2 Oscars. Another 3 wins & 4 nominations."             
[1367] "Nominated for 3 Oscars. Another 8 wins & 4 nominations."             
[1368] "Won 2 Oscars. Another 8 wins & 15 nominations."                      
[1369] "Won 1 BAFTA Film Award. Another 2 wins & 4 nominations."             
[1370] "Nominated for 2 Golden Globes. Another 1 win & 1 nomination."        
[1371] "Nominated for 1 Oscar. Another 24 wins & 17 nominations."            
[1372] "Nominated for 6 Oscars. Another 4 wins & 6 nominations."             
[1373] "Nominated for 6 Oscars. Another 2 wins & 10 nominations."            
[1374] "Won 9 Oscars. Another 12 wins & 9 nominations."                      
[1375] "Nominated for 2 Oscars. Another 3 wins & 2 nominations."             
[1376] "Won 1 Oscar. Another 5 wins & 15 nominations."                       
[1377] "Nominated for 3 Golden Globes. Another 3 nominations."               
[1378] "Won 1 Golden Globe. Another 2 wins & 2 nominations."                 
[1379] "Won 3 BAFTA Film Awards. Another 3 nominations."                     
[1380] "Won 2 Oscars. Another 5 wins & 15 nominations."                      
[1381] "Nominated for 5 Oscars. Another 3 wins & 5 nominations."             
[1382] "11 wins & 13 nominations."                                           
[1383] "Won 1 Golden Globe. Another 4 wins & 1 nomination."                  
[1384] "Won 1 Oscar. Another 5 nominations."                                 
[1385] "Nominated for 3 Oscars. Another 16 wins & 8 nominations."            
[1386] "Nominated for 2 Oscars. Another 3 wins & 30 nominations."            
[1387] "Nominated for 2 BAFTA Film Awards. Another 6 wins."                  
[1388] "Nominated for 2 Golden Globes. Another 1 win & 4 nominations."       
[1389] "Nominated for 4 Oscars. Another 2 wins & 6 nominations."             
[1390] "Nominated for 3 Golden Globes. Another 3 wins & 1 nomination."       
[1391] "Nominated for 6 Oscars. Another 1 win."                              
[1392] "Nominated for 9 Oscars. Another 2 wins & 8 nominations."             
[1393] "Nominated for 4 Oscars. Another 1 win & 4 nominations."              
[1394] "Won 4 Oscars. Another 4 wins & 18 nominations."                      
[1395] "Won 1 Golden Globe. Another 3 wins & 5 nominations."                 
[1396] "Nominated for 6 Oscars. Another 3 wins & 9 nominations."             
[1397] "Won 1 Golden Globe. Another 3 wins & 3 nominations."                 
[1398] "Won 5 Oscars. Another 8 wins & 5 nominations."                       
[1399] "Nominated for 6 Oscars. Another 5 wins & 4 nominations."             
[1400] "Nominated for 1 Oscar. Another 10 wins & 2 nominations."             
[1401] "Won 1 Oscar. Another 6 wins & 15 nominations."                       
[1402] "Won 1 BAFTA Film Award. Another 4 nominations."                      
[1403] "Won 2 Oscars. Another 2 wins & 4 nominations."                       
[1404] "Nominated for 3 Oscars. Another 5 wins & 10 nominations."            
[1405] "Nominated for 4 Oscars. Another 2 nominations."                      
[1406] "Nominated for 3 Oscars. Another 1 nomination."                       
[1407] "Won 1 Oscar. Another 11 wins & 10 nominations."                      
[1408] "Won 1 Oscar. Another 2 wins & 5 nominations."                        
[1409] "Won 3 Oscars. Another 2 wins & 6 nominations."                       
[1410] "Won 4 Oscars. Another 14 wins & 5 nominations."                      
[1411] "Won 2 Oscars. Another 1 win & 4 nominations."                        
[1412] "Won 1 Golden Globe. Another 6 wins & 1 nomination."                  
[1413] "Nominated for 1 BAFTA Film Award. Another 8 wins & 1 nomination."    
[1414] "Nominated for 3 Oscars. Another 2 wins & 3 nominations."             
[1415] "Nominated for 3 Oscars. Another 1 win."                              
[1416] "Won 1 BAFTA Film Award. Another 3 nominations."                      
[1417] "Nominated for 4 Oscars. Another 3 wins & 5 nominations."             
[1418] "Won 8 Oscars. Another 21 wins & 9 nominations."                      
[1419] "Won 2 Oscars. Another 3 nominations."                                
[1420] "Won 1 Oscar. Another 2 nominations."                                 
[1421] "Won 8 Oscars. Another 14 wins & 7 nominations."                      
[1422] "Won 1 Oscar. Another 6 wins & 8 nominations."                        
[1423] "7 wins & 26 nominations."                                            
[1424] "Won 3 Oscars. Another 7 wins & 15 nominations."                      
[1425] "Nominated for 6 Oscars. Another 4 nominations."                      
[1426] "Won 4 Oscars. Another 13 wins & 10 nominations."                     
[1427] "Nominated for 1 BAFTA Film Award. Another 5 wins."                   
[1428] "Nominated for 3 Oscars. Another 5 nominations."                      
[1429] "Won 2 Oscars. Another 4 wins & 10 nominations."                      
[1430] "Nominated for 4 Oscars. Another 1 win & 1 nomination."               
[1431] "Nominated for 2 Oscars. Another 5 wins & 7 nominations."             
[1432] "30 wins & 2 nominations."                                            
[1433] "Nominated for 5 Oscars. Another 1 win & 3 nominations."              
[1434] "Won 1 Golden Globe. Another 6 wins & 11 nominations."                
[1435] "Nominated for 1 Primetime Emmy. Another 1 win & 26 nominations."     
[1436] "Nominated for 2 Oscars. Another 19 wins & 61 nominations."           
[1437] "Nominated for 8 Oscars. Another 4 wins & 3 nominations."             
[1438] "Won 6 Oscars. Another 16 wins & 17 nominations."                     
[1439] "Nominated for 1 Oscar. Another 12 wins & 13 nominations."            
[1440] "Won 2 Oscars. Another 3 wins & 5 nominations."                       
[1441] "Nominated for 7 Oscars. Another 2 nominations."                      
[1442] "Nominated for 2 Golden Globes. Another 2 wins & 6 nominations."      
[1443] "Won 2 Oscars. Another 1 win & 5 nominations."                        
[1444] "Nominated for 4 Oscars. Another 1 win."                              
[1445] "Won 2 Oscars. Another 3 wins & 3 nominations."                       
[1446] "Won 1 Oscar. Another 4 wins & 13 nominations."                       
[1447] "Won 3 Oscars. Another 87 wins & 131 nominations."                    
[1448] "9 wins & 6 nominations."                                             
[1449] "24 wins & 5 nominations."                                            
[1450] "22 wins & 17 nominations."                                           
[1451] "Won 3 Oscars. Another 8 wins & 8 nominations."                       
[1452] "Nominated for 4 Oscars. Another 2 wins."                             
[1453] "Nominated for 5 Oscars. Another 6 wins & 1 nomination."              
[1454] "Nominated for 4 Oscars. Another 2 wins & 4 nominations."             
[1455] "Nominated for 1 Oscar. Another 9 wins & 13 nominations."             
[1456] "Nominated for 7 Oscars. Another 2 wins & 2 nominations."             
[1457] "Won 2 Oscars. Another 1 win & 7 nominations."                        
[1458] "Won 7 Oscars. Another 10 wins & 4 nominations."                      
[1459] "Nominated for 3 Oscars. Another 2 wins."                             
[1460] "Nominated for 4 Oscars. Another 5 wins & 1 nomination."              
[1461] "Won 5 Oscars. Another 2 wins & 7 nominations."                       
[1462] "Nominated for 4 Oscars. Another 1 nomination."                       
[1463] "Won 1 Oscar. Another 4 wins & 3 nominations."                        
[1464] "Nominated for 3 Oscars. Another 4 wins & 1 nomination."              
[1465] "Nominated for 2 Oscars. Another 5 wins & 1 nomination."              
[1466] "Won 6 Oscars. Another 2 wins & 7 nominations."                       
[1467] "Nominated for 7 Oscars. Another 1 nomination."                       
[1468] "Nominated for 4 Oscars. Another 2 wins & 2 nominations."             
[1469] "Won 1 Oscar. Another 7 wins & 12 nominations."                       
[1470] "Won 2 Oscars. Another 1 win & 6 nominations."                        
[1471] "Nominated for 6 Oscars. Another 1 nomination."                       
[1472] "Won 5 Oscars. Another 5 wins & 6 nominations."                       
[1473] "9 wins & 18 nominations."                                            
[1474] "Won 2 Oscars. Another 2 wins & 10 nominations."                      
[1475] "Nominated for 1 Primetime Emmy. Another 3 nominations."              
[1476] "Nominated for 6 Oscars. Another 1 win & 1 nomination."               
[1477] "Nominated for 6 Oscars. Another 75 wins & 153 nominations."          
[1478] "Won 1 Golden Globe. Another 6 wins & 21 nominations."                
[1479] "Won 8 Oscars. Another 9 wins & 8 nominations."                       
[1480] "Nominated for 1 Oscar. Another 3 wins & 28 nominations."             
[1481] "Won 2 Oscars. Another 4 nominations."                                
[1482] "Nominated for 5 Oscars. Another 2 nominations."                      
[1483] "Won 2 Oscars. Another 3 wins & 4 nominations."                       
[1484] "Won 1 Oscar. Another 15 wins & 20 nominations."                      
[1485] "Won 2 Oscars. Another 6 nominations."                                
[1486] "Nominated for 1 Oscar. Another 11 wins & 4 nominations."             
[1487] "Nominated for 4 Oscars. Another 2 wins & 1 nomination."              
[1488] "Won 4 Oscars. Another 3 nominations."                                
[1489] "5 wins & 19 nominations."                                            
[1490] "Won 1 Oscar. Another 4 wins & 6 nominations."                        
[1491] "Won 1 Oscar. Another 30 wins & 72 nominations."                      
[1492] "Won 5 Oscars. Another 5 wins & 1 nomination."                        
[1493] "Won 3 Oscars. Another 1 win & 1 nomination."                         
[1494] "Won 2 Oscars. Another 1 nomination."                                 
[1495] "Won 2 Oscars. Another 5 wins & 2 nominations."                       
[1496] "Nominated for 4 Oscars. Another 3 wins."                             
[1497] "Nominated for 2 Oscars. Another 16 wins & 41 nominations."           
[1498] "3 wins & 43 nominations."                                            
[1499] "Nominated for 1 Oscar. Another 7 wins & 42 nominations."             
[1500] "4 wins & 28 nominations."                                            
[1501] "Won 1 Golden Globe. Another 3 wins & 1 nomination."                  
[1502] "Won 2 Oscars. Another 1 win & 2 nominations."                        
[1503] "Nominated for 2 Oscars. Another 6 wins & 6 nominations."             
[1504] "Nominated for 1 Oscar. Another 19 wins & 68 nominations."            
[1505] "Won 1 Oscar. Another 32 wins & 101 nominations."                     
[1506] "Won 3 Oscars. Another 74 wins & 160 nominations."                    
[1507] "Nominated for 1 Oscar. Another 45 wins & 55 nominations."            
[1508] "Nominated for 1 Golden Globe. Another 5 wins & 39 nominations."      
[1509] "14 wins & 29 nominations."                                           
[1510] "Won 2 Oscars. Another 112 wins & 124 nominations."                   
[1511] "Nominated for 1 Golden Globe. Another 1 win & 43 nominations."       
[1512] "Won 1 Oscar. Another 7 wins & 26 nominations."                       
[1513] "Nominated for 3 Oscars. Another 30 wins & 141 nominations."          
[1514] "Won 1 Oscar. Another 99 wins & 118 nominations."                     
[1515] "Won 1 Oscar. Another 28 wins & 88 nominations."                      
[1516] "Nominated for 7 Oscars. Another 34 wins & 168 nominations."          
[1517] "Nominated for 3 Oscars. Another 12 wins & 129 nominations."          
[1518] "Nominated for 2 Golden Globes. Another 3 wins & 13 nominations."     
[1519] "Nominated for 1 Oscar. Another 24 wins & 39 nominations."            
[1520] "15 wins & 31 nominations."                                           
[1521] "Nominated for 2 Golden Globes. Another 5 wins & 21 nominations."     
[1522] "Nominated for 1 BAFTA Film Award. Another 2 wins & 27 nominations."  
[1523] "Nominated for 1 BAFTA Film Award. Another 18 nominations."           
[1524] "5 wins & 52 nominations."                                            
[1525] "1 win & 15 nominations."                                             
[1526] "3 wins & 40 nominations."                                            
[1527] "13 wins & 12 nominations."                                           
[1528] "Won 1 Oscar. Another 61 wins & 133 nominations."                     
[1529] "Nominated for 1 Golden Globe. Another 20 wins & 25 nominations."     
[1530] "20 wins & 29 nominations."                                           
[1531] "Won 1 Oscar. Another 28 wins & 32 nominations."                      
[1532] "Nominated for 1 Golden Globe. Another 14 wins & 44 nominations."     
[1533] "Won 1 Golden Globe. Another 1 win & 17 nominations."                 
[1534] "Won 1 Oscar. Another 56 wins & 87 nominations."                      
[1535] "Nominated for 3 Oscars. Another 11 wins & 69 nominations."           
[1536] "Won 1 Oscar. Another 18 wins & 38 nominations."                      
[1537] "Nominated for 4 Oscars. Another 19 wins & 57 nominations."           
[1538] "Nominated for 1 Oscar. Another 6 wins & 45 nominations."             
[1539] "7 wins & 19 nominations."                                            
[1540] "Nominated for 2 Oscars. Another 14 wins & 90 nominations."           
[1541] "Nominated for 2 Oscars. Another 10 wins & 61 nominations."           
[1542] "17 wins & 3 nominations."                                            
[1543] "Won 1 Oscar. Another 44 wins & 149 nominations."                     
[1544] "Nominated for 1 Golden Globe. Another 15 wins & 26 nominations."     
[1545] "Nominated for 1 Oscar. Another 4 wins & 21 nominations."             
[1546] "Won 1 Oscar. Another 24 wins & 117 nominations."                     
[1547] "Won 1 Oscar. Another 13 wins & 53 nominations."                      
[1548] "Won 1 Oscar. Another 39 wins & 132 nominations."                     
[1549] "Nominated for 1 Oscar. Another 42 wins & 116 nominations."           
[1550] "Won 4 Oscars. Another 182 wins & 264 nominations."                   
[1551] "Nominated for 1 Golden Globe. Another 2 wins & 27 nominations."      
[1552] "Nominated for 2 Golden Globes. Another 6 wins & 18 nominations."     
[1553] "Nominated for 1 Oscar. Another 8 wins & 58 nominations."             
[1554] "Nominated for 2 Oscars. Another 48 wins & 92 nominations."           
[1555] "Nominated for 1 Oscar. Another 2 wins & 11 nominations."             
[1556] "Won 1 Oscar. Another 169 wins & 202 nominations."                    
[1557] "Nominated for 1 Oscar. Another 2 wins & 16 nominations."             
[1558] "19 wins & 94 nominations."                                           
[1559] "5 wins & 22 nominations."                                            
[1560] "7 wins & 22 nominations."                                            
[1561] "Nominated for 1 Oscar. Another 13 wins & 59 nominations."            
[1562] "9 wins & 28 nominations."                                            
[1563] "11 wins & 34 nominations."                                           
[1564] "Nominated for 1 Oscar. Another 9 wins & 40 nominations."             
[1565] "Nominated for 1 Oscar. Another 15 wins & 40 nominations."            
[1566] "15 wins & 30 nominations."                                           
[1567] "Won 1 Oscar. Another 65 wins & 82 nominations."                      
[1568] "3 wins & 29 nominations."                                            
[1569] "1 win & 19 nominations."                                             
[1570] "Nominated for 1 Golden Globe. Another 16 nominations."               
[1571] "Won 4 Oscars. Another 121 wins & 213 nominations."                   
[1572] "Nominated for 1 BAFTA Film Award. Another 28 wins & 35 nominations." 
[1573] "Nominated for 1 Oscar. Another 69 wins & 59 nominations."            
[1574] "14 wins & 25 nominations."                                           
[1575] "Nominated for 2 Oscars. Another 15 wins & 61 nominations."           
[1576] "Nominated for 2 Oscars. Another 5 wins & 14 nominations."            
[1577] "Nominated for 5 Oscars. Another 35 wins & 154 nominations."          
[1578] "Won 1 Oscar. Another 76 wins & 174 nominations."                     
[1579] "1 win & 23 nominations."                                             
[1580] "Nominated for 1 Oscar. Another 12 wins & 71 nominations."            
[1581] "Nominated for 10 Oscars. Another 65 wins & 202 nominations."         
[1582] "Nominated for 3 Oscars. Another 14 wins & 80 nominations."           
[1583] "Nominated for 2 Oscars. Another 46 wins & 157 nominations."          
[1584] "Nominated for 1 Oscar. Another 10 wins & 29 nominations."            
[1585] "Nominated for 4 Oscars. Another 31 wins & 75 nominations."           
[1586] "Nominated for 1 Golden Globe. Another 21 wins & 59 nominations."     
[1587] "Nominated for 6 Oscars. Another 26 wins & 157 nominations."          
[1588] "Won 3 Oscars. Another 79 wins & 78 nominations."                     
[1589] "Nominated for 1 Oscar. Another 3 wins & 45 nominations."             
[1590] "Won 3 Oscars. Another 233 wins & 312 nominations."                   
[1591] "Nominated for 6 Oscars. Another 16 wins & 138 nominations."          
[1592] "1 win & 28 nominations."                                             
[1593] "Nominated for 1 Oscar. Another 10 wins & 34 nominations."            
[1594] "Nominated for 1 Golden Globe. Another 8 wins & 36 nominations."      
[1595] "Nominated for 2 BAFTA Film Awards. Another 17 wins & 50 nominations."
[1596] "10 wins & 30 nominations."                                           
[1597] "Nominated for 1 Oscar. Another 51 wins & 40 nominations."            
[1598] "10 wins & 20 nominations."                                           
[1599] "14 wins & 21 nominations."                                           
[1600] "39 wins & 52 nominations."                                           
[1601] "Nominated for 1 BAFTA Film Award. Another 4 wins & 42 nominations."  
[1602] "5 wins & 30 nominations."                                            
[1603] "Nominated for 2 Oscars. Another 12 wins & 63 nominations."           
[1604] "Nominated for 2 Oscars. Another 4 wins & 17 nominations."            
[1605] "3 wins & 23 nominations."                                            
[1606] "Nominated for 1 BAFTA Film Award. Another 9 wins & 54 nominations."  
[1607] "Won 1 Oscar. Another 20 wins & 25 nominations."                      
[1608] "7 wins & 42 nominations."                                            
[1609] "22 wins & 92 nominations."                                           
[1610] "Nominated for 1 Oscar. Another 19 wins & 57 nominations."            
[1611] "2 wins & 21 nominations."                                            
[1612] "Nominated for 1 Oscar. Another 6 wins & 46 nominations."             
[1613] "Nominated for 1 Oscar. Another 17 wins & 53 nominations."            
[1614] "3 wins & 17 nominations."                                            
[1615] "6 wins & 16 nominations."                                            
[1616] "11 wins & 26 nominations."                                           
[1617] "5 wins & 31 nominations."                                            
[1618] "4 wins & 34 nominations."                                            
[1619] "Won 2 Oscars. Another 53 wins & 143 nominations."                    
[1620] "Nominated for 1 Oscar. Another 27 wins & 65 nominations."            
[1621] "Won 1 Oscar. Another 76 wins & 101 nominations."                     
[1622] "Won 1 Oscar. Another 84 wins & 167 nominations."                     
[1623] "Nominated for 3 Oscars. Another 9 wins & 70 nominations."            
[1624] "Won 4 Oscars. Another 76 wins & 125 nominations."                    
[1625] "Nominated for 1 Golden Globe. Another 14 wins & 30 nominations."     
[1626] "Won 1 Oscar. Another 86 wins & 145 nominations."                     
[1627] "20 wins & 11 nominations."                                           
[1628] "13 wins & 19 nominations."                                           
[1629] "Won 2 Oscars. Another 108 wins & 242 nominations."                   
[1630] "Won 2 Oscars. Another 64 wins & 115 nominations."                    
[1631] "Nominated for 1 Oscar. Another 33 wins & 40 nominations."            
[1632] "Nominated for 1 Golden Globe. Another 16 wins & 72 nominations."     
[1633] "Nominated for 1 Oscar. Another 18 wins & 62 nominations."            
[1634] "28 wins & 68 nominations."                                           
[1635] "3 wins & 21 nominations."                                            
[1636] "Won 3 Oscars. Another 91 wins & 150 nominations."                    
[1637] "Nominated for 1 Oscar. Another 5 wins & 41 nominations."             
[1638] "6 wins & 20 nominations."                                            
[1639] "15 wins & 43 nominations."                                           
[1640] "18 wins & 48 nominations."                                           
[1641] "Nominated for 3 Oscars. Another 76 wins & 179 nominations."          
[1642] "7 wins & 33 nominations."                                            
[1643] "Nominated for 1 Oscar. Another 19 wins & 44 nominations."            
[1644] "9 wins & 20 nominations."                                            
[1645] "Nominated for 1 BAFTA Film Award. Another 38 wins & 96 nominations." 
[1646] "2 wins & 31 nominations."                                            
[1647] "8 wins & 13 nominations."                                            
[1648] "Nominated for 4 Oscars. Another 91 wins & 123 nominations."          
[1649] "Nominated for 1 Oscar. Another 6 wins & 43 nominations."             
[1650] "1 win & 20 nominations."                                             
[1651] "Nominated for 2 Oscars. Another 12 wins & 31 nominations."           
[1652] "Nominated for 1 Oscar. Another 37 wins & 116 nominations."           
[1653] "Nominated for 2 Golden Globes. Another 4 wins & 19 nominations."     
[1654] "Nominated for 1 Oscar. Another 34 wins & 75 nominations."            
[1655] "Nominated for 1 Oscar. Another 19 nominations."                      
[1656] "18 wins & 33 nominations."                                           
[1657] "Nominated for 1 Golden Globe. Another 33 wins & 42 nominations."     
[1658] "11 wins & 18 nominations."                                           
[1659] "Nominated for 1 Golden Globe. Another 1 win & 6 nominations."        
[1660] "12 wins & 4 nominations."                                            
[1661] "10 wins & 15 nominations."                                           
[1662] "Won 2 Oscars. Another 23 wins & 46 nominations."                     
[1663] "Nominated for 6 Oscars. Another 16 wins & 70 nominations."           
[1664] "Nominated for 3 Oscars. Another 18 wins & 39 nominations."           
[1665] "Nominated for 1 Oscar. Another 22 wins & 59 nominations."            
[1666] "Won 1 Oscar. Another 26 wins & 85 nominations."                      
[1667] "Nominated for 1 Golden Globe. Another 27 wins & 61 nominations."     
[1668] "Nominated for 1 Golden Globe. Another 3 wins & 29 nominations."      
[1669] "Nominated for 3 Oscars. Another 34 wins & 88 nominations."           
[1670] "Nominated for 1 BAFTA Film Award. Another 10 wins & 16 nominations." 
[1671] "Won 5 Oscars. Another 146 wins & 185 nominations."                   
[1672] "Nominated for 2 Oscars. Another 19 wins & 59 nominations."           
[1673] "Nominated for 1 Golden Globe. Another 18 wins & 26 nominations."     
[1674] "Won 1 Oscar. Another 15 wins & 41 nominations."                      
[1675] "Won 5 Oscars. Another 53 wins & 175 nominations."                    
[1676] "11 wins & 21 nominations."                                           
[1677] "Won 1 Oscar. Another 65 wins & 140 nominations."                     
[1678] "Nominated for 1 Golden Globe. Another 5 wins & 16 nominations."      
[1679] "22 wins & 68 nominations."                                           
[1680] "Nominated for 1 Oscar. Another 8 wins & 23 nominations."             
[1681] "15 wins & 32 nominations."                                           
[1682] "Nominated for 1 Oscar. Another 9 wins & 34 nominations."             
[1683] "41 wins & 35 nominations."                                           
[1684] "Nominated for 6 Oscars. Another 29 wins & 74 nominations."           
[1685] "Won 1 Oscar. Another 79 wins & 112 nominations."                     
[1686] "Nominated for 1 Oscar. Another 20 wins & 41 nominations."            
[1687] "Nominated for 1 Golden Globe. Another 14 wins & 25 nominations."     
[1688] "Nominated for 1 BAFTA Film Award. Another 17 wins & 35 nominations." 
[1689] "Nominated for 1 Golden Globe. Another 3 wins & 20 nominations."      
[1690] "2 wins & 25 nominations."                                            
[1691] "Nominated for 3 Oscars. Another 10 wins & 40 nominations."           
[1692] "Nominated for 1 Golden Globe. Another 1 win & 16 nominations."       
[1693] "11 wins & 62 nominations."                                           
[1694] "Won 1 Oscar. Another 34 wins & 27 nominations."                      
[1695] "20 wins & 35 nominations."                                           
[1696] "Nominated for 1 Oscar. Another 5 wins & 44 nominations."             
[1697] "Won 1 Oscar. Another 22 wins & 96 nominations."                      
[1698] "2 wins & 30 nominations."                                            
[1699] "Nominated for 2 Oscars. Another 25 wins & 67 nominations."           
[1700] "5 wins & 29 nominations."                                            
[1701] "5 wins & 26 nominations."                                            
[1702] "Won 1 Oscar. Another 45 wins & 24 nominations."                      
[1703] "Nominated for 1 Oscar. Another 7 wins & 50 nominations."             
[1704] "Nominated for 1 Oscar. Another 20 wins & 52 nominations."            
[1705] "Nominated for 1 Oscar. Another 7 wins & 40 nominations."             
[1706] "Nominated for 1 Oscar. Another 18 wins & 17 nominations."            
[1707] "Nominated for 1 Oscar. Another 10 wins & 48 nominations."            
[1708] "Won 2 Oscars. Another 69 wins & 113 nominations."                    
[1709] "Nominated for 3 Golden Globes. Another 3 wins & 2 nominations."      
[1710] "Won 4 Oscars. Another 100 wins & 190 nominations."                   
[1711] "Won 1 Golden Globe. Another 5 wins & 17 nominations."                
[1712] "Nominated for 3 BAFTA Film Awards. Another 3 wins & 14 nominations." 
[1713] "Won 1 BAFTA Film Award. Another 8 wins & 23 nominations."            
[1714] "Nominated for 6 Oscars. Another 23 wins & 140 nominations."          
[1715] "14 wins & 16 nominations."                                           
[1716] "Nominated for 3 BAFTA Film Awards. Another 6 wins & 18 nominations." 
[1717] "14 wins & 28 nominations."                                           
[1718] "Won 3 Oscars. Another 161 wins & 162 nominations."                   
[1719] "4 wins & 23 nominations."                                            
[1720] "Nominated for 1 Oscar. Another 39 wins & 15 nominations."            
[1721] "Nominated for 1 Golden Globe. Another 9 wins & 19 nominations."      
[1722] "Nominated for 1 Oscar. Another 9 wins & 44 nominations."             
[1723] "Nominated for 1 Oscar. Another 38 wins & 54 nominations."            
[1724] "16 wins & 58 nominations."                                           
[1725] "5 wins & 27 nominations."                                            
[1726] "Won 4 Oscars. Another 143 wins & 198 nominations."                   
[1727] "Nominated for 4 Oscars. Another 29 wins & 116 nominations."          
[1728] "Nominated for 1 Golden Globe. Another 3 wins & 38 nominations."      
[1729] "23 wins & 33 nominations."                                           
[1730] "Won 2 Oscars. Another 57 wins & 89 nominations."                     
[1731] "Nominated for 4 Oscars. Another 63 wins & 118 nominations."          
[1732] "11 wins & 14 nominations."                                           
[1733] "16 wins & 61 nominations."                                           
[1734] "17 wins & 24 nominations."                                           
[1735] "Nominated for 2 Oscars. Another 25 wins & 58 nominations."           
[1736] "Nominated for 1 Oscar. Another 7 wins & 5 nominations."              
[1737] "32 wins & 51 nominations."                                           
[1738] "Nominated for 1 Oscar. Another 15 wins & 8 nominations."             
[1739] "Nominated for 2 Oscars. Another 18 wins & 45 nominations."           
[1740] "20 wins & 27 nominations."                                           
[1741] "Nominated for 2 Oscars. Another 3 wins & 15 nominations."            
[1742] "Nominated for 2 Oscars. Another 4 wins & 20 nominations."            
[1743] "Nominated for 3 Golden Globes. Another 8 wins & 12 nominations."     
[1744] "Won 1 Oscar. Another 12 wins & 16 nominations."                      
[1745] "Nominated for 4 Oscars. Another 8 wins & 56 nominations."            
[1746] "Won 2 Oscars. Another 35 wins & 29 nominations."                     
[1747] "Nominated for 1 Oscar. Another 34 wins & 51 nominations."            
[1748] "Nominated for 2 Oscars. Another 10 wins & 33 nominations."           
[1749] "Nominated for 1 BAFTA Film Award. Another 5 wins & 25 nominations."  
[1750] "Nominated for 1 BAFTA Film Award. Another 5 wins & 30 nominations."  
[1751] "Nominated for 3 Oscars. Another 12 wins & 39 nominations."           
[1752] "Won 1 Oscar. Another 7 wins & 29 nominations."                       
[1753] "13 wins & 43 nominations."                                           
[1754] "17 wins & 23 nominations."                                           
[1755] "Nominated for 2 Oscars. Another 15 wins & 44 nominations."           
[1756] "Nominated for 1 Golden Globe. Another 7 wins & 48 nominations."      
[1757] "Nominated for 3 Oscars. Another 34 wins & 90 nominations."           
[1758] "Nominated for 2 Oscars. Another 17 wins & 70 nominations."           
[1759] "9 wins & 26 nominations."                                            
[1760] "Nominated for 2 Golden Globes. Another 1 win & 17 nominations."      
[1761] "Nominated for 1 Oscar. Another 15 wins & 49 nominations."            
[1762] "Won 1 Oscar. Another 123 wins & 154 nominations."                    
[1763] "Nominated for 4 Oscars. Another 24 wins & 104 nominations."          
[1764] "Nominated for 1 Oscar. Another 24 wins & 46 nominations."            
[1765] "Nominated for 1 Oscar. Another 8 wins & 30 nominations."             
[1766] "Won 6 Oscars. Another 114 wins & 121 nominations."                   
[1767] "Nominated for 1 Oscar. Another 13 wins & 25 nominations."            
[1768] "Nominated for 1 Golden Globe. Another 7 wins & 18 nominations."      
[1769] "Nominated for 1 Oscar. Another 7 wins & 19 nominations."             
[1770] "Won 1 BAFTA Film Award. Another 24 wins & 31 nominations."           
[1771] "Won 1 Golden Globe. Another 10 wins & 26 nominations."               
[1772] "Won 2 Oscars. Another 73 wins & 75 nominations."                     
[1773] "Won 1 Oscar. Another 22 wins & 83 nominations."                      
[1774] "12 wins & 20 nominations."                                           
[1775] "Nominated for 1 Oscar. Another 7 wins & 41 nominations."             
[1776] "Nominated for 3 Oscars. Another 19 wins & 62 nominations."           
[1777] "Nominated for 1 Oscar. Another 43 wins & 56 nominations."            
[1778] "Won 3 Oscars. Another 73 wins & 146 nominations."                    
[1779] "Nominated for 2 Oscars. Another 56 wins & 83 nominations."           
[1780] "Nominated for 5 Oscars. Another 26 wins & 88 nominations."           
[1781] "Nominated for 1 Golden Globe. Another 20 wins & 17 nominations."     
[1782] "Won 1 Oscar. Another 24 wins & 47 nominations."                      
[1783] "Nominated for 1 Golden Globe. Another 7 wins & 19 nominations."      
[1784] "Nominated for 5 Oscars. Another 21 wins & 72 nominations."           
[1785] "Won 2 Oscars. Another 57 wins & 137 nominations."                    
[1786] "Nominated for 1 Oscar. Another 1 win & 28 nominations."              
[1787] "Nominated for 2 BAFTA Film Awards. Another 4 wins & 29 nominations." 
[1788] "Won 8 Oscars. Another 143 wins & 120 nominations."                   
[1789] "8 wins & 25 nominations."                                            
[1790] "Nominated for 3 Oscars. Another 13 wins & 47 nominations."           
[1791] "12 wins & 16 nominations."                                           
[1792] "Nominated for 1 Oscar. Another 31 wins & 59 nominations."            
[1793] "Won 1 Oscar. Another 7 wins & 20 nominations."                       
[1794] "Nominated for 2 Golden Globes. Another 5 wins & 26 nominations."     
[1795] "Won 1 Oscar. Another 31 wins & 51 nominations."                      
[1796] "Nominated for 1 Oscar. Another 10 wins & 39 nominations."            
[1797] "Nominated for 2 Oscars. Another 32 wins & 36 nominations."           
[1798] "Won 1 Oscar. Another 44 wins & 11 nominations."                      
[1799] "Nominated for 2 Golden Globes. Another 15 wins & 24 nominations."    
[1800] "Won 2 Oscars. Another 146 wins & 142 nominations."                   
[1801] "Nominated for 1 Oscar. Another 5 wins & 25 nominations."             
[1802] "Nominated for 1 Oscar. Another 13 wins & 8 nominations."             
[1803] "Nominated for 1 Oscar. Another 14 wins & 38 nominations."            
[1804] "Nominated for 1 BAFTA Film Award. Another 9 wins & 34 nominations."  
[1805] "13 nominations."                                                     
[1806] "Nominated for 1 Oscar. Another 18 wins & 33 nominations."            
[1807] "Nominated for 1 Oscar. Another 22 wins & 51 nominations."            
[1808] "5 wins & 23 nominations."                                            
[1809] "Won 2 Oscars. Another 105 wins & 133 nominations."                   
[1810] "Nominated for 1 Oscar. Another 28 wins & 54 nominations."            
[1811] "Nominated for 1 Golden Globe. Another 11 wins & 9 nominations."      
[1812] "Nominated for 1 Oscar. Another 5 wins & 22 nominations."             
[1813] "Nominated for 1 Oscar. Another 6 wins & 22 nominations."             
[1814] "9 wins & 21 nominations."                                            
[1815] "Nominated for 2 Primetime Emmys. Another 4 wins & 7 nominations."    
[1816] "Won 1 Oscar. Another 48 wins & 145 nominations."                     
[1817] "Won 1 Oscar. Another 88 wins & 94 nominations."                      
[1818] "Won 1 Oscar. Another 4 wins & 32 nominations."                       
[1819] "Nominated for 2 Oscars. Another 17 wins & 31 nominations."           
[1820] "Nominated for 2 Oscars. Another 12 wins & 34 nominations."           
[1821] "Nominated for 1 Golden Globe. Another 1 win & 14 nominations."       
[1822] "Nominated for 1 Oscar. Another 32 wins & 28 nominations."            
[1823] "Nominated for 1 Oscar. Another 7 wins & 32 nominations."             
[1824] "Won 1 Oscar. Another 5 wins & 29 nominations."                       
[1825] "Nominated for 1 BAFTA Film Award. Another 30 wins & 32 nominations." 
[1826] "Won 1 Oscar. Another 26 wins & 109 nominations."                     
[1827] "Nominated for 2 Oscars. Another 23 wins & 99 nominations."           
[1828] "Nominated for 2 Oscars. Another 25 wins & 64 nominations."           
[1829] "Nominated for 1 Oscar. Another 2 wins & 17 nominations."             
[1830] "Nominated for 1 Oscar. Another 28 wins & 69 nominations."            
[1831] "11 wins & 24 nominations."                                           
[1832] "Won 3 Oscars. Another 25 wins & 38 nominations."                     
[1833] "Nominated for 1 Oscar. Another 12 wins & 18 nominations."            
[1834] "Nominated for 1 Golden Globe. Another 4 wins & 32 nominations."      
[1835] "Nominated for 1 BAFTA Film Award. Another 13 wins & 15 nominations." 
[1836] "Nominated for 2 BAFTA Film Awards. Another 14 wins & 38 nominations."
[1837] "Nominated for 3 Oscars. Another 19 wins & 40 nominations."           
[1838] "Won 1 Oscar. Another 64 wins & 42 nominations."                      
[1839] "Nominated for 1 Oscar. Another 14 wins & 14 nominations."            
[1840] "Nominated for 1 Golden Globe. Another 4 wins & 17 nominations."      
[1841] "13 wins & 15 nominations."                                           
[1842] "Nominated for 1 Oscar. Another 2 wins & 18 nominations."             
[1843] "7 wins & 25 nominations."                                            
[1844] "Nominated for 2 Oscars. Another 20 wins & 41 nominations."           
[1845] "Nominated for 1 BAFTA Film Award. Another 5 wins & 14 nominations."  
[1846] "Nominated for 1 BAFTA Film Award. Another 3 wins & 31 nominations."  
[1847] "11 wins & 20 nominations."                                           
[1848] "6 wins & 23 nominations."                                            
[1849] "16 wins & 42 nominations."                                           
[1850] "2 wins & 64 nominations."                                            
[1851] "Nominated for 1 Golden Globe. Another 5 wins & 4 nominations."       
[1852] "15 wins & 17 nominations."                                           
[1853] "Nominated for 4 Oscars. Another 14 wins & 65 nominations."           
[1854] "Nominated for 3 Oscars. Another 41 wins & 71 nominations."           
[1855] "Nominated for 1 Oscar. Another 4 wins & 20 nominations."             
[1856] "Won 1 Golden Globe. Another 8 wins & 11 nominations."                
[1857] "Nominated for 1 Oscar. Another 11 wins & 24 nominations."            
[1858] "Won 2 Oscars. Another 66 wins & 87 nominations."                     
[1859] "Nominated for 3 Oscars. Another 8 wins & 17 nominations."            
[1860] "Nominated for 1 Golden Globe. Another 8 wins & 31 nominations."      
[1861] "Nominated for 2 BAFTA Film Awards. Another 2 wins & 12 nominations." 
[1862] "Won 1 Oscar. Another 18 wins & 24 nominations."                      
[1863] "Nominated for 1 Oscar. Another 60 wins & 80 nominations."            
[1864] "Nominated for 1 Oscar. Another 20 wins & 29 nominations."            
[1865] "Nominated for 1 Golden Globe. Another 3 wins & 6 nominations."       
[1866] "Nominated for 2 Oscars. Another 5 wins & 32 nominations."            
[1867] "Nominated for 2 Oscars. Another 15 wins & 24 nominations."           
[1868] "Nominated for 3 Oscars. Another 18 wins & 54 nominations."           
[1869] "Won 4 Oscars. Another 90 wins & 123 nominations."                    
[1870] "Won 1 Oscar. Another 90 wins & 92 nominations."                      
[1871] "Won 1 Oscar. Another 47 wins & 30 nominations."                      
[1872] "Nominated for 1 Golden Globe. Another 10 wins & 10 nominations."     
[1873] "14 wins & 13 nominations."                                           
[1874] "Nominated for 1 Oscar. Another 10 wins & 9 nominations."             
[1875] "Nominated for 1 Oscar. Another 27 wins & 45 nominations."            
[1876] "4 wins & 22 nominations."                                            
[1877] "Nominated for 1 Oscar. Another 4 wins & 23 nominations."             
[1878] "Won 1 Oscar. Another 42 wins & 51 nominations."                      
[1879] "Nominated for 1 Oscar. Another 12 wins & 38 nominations."            
[1880] "Nominated for 2 Oscars. Another 27 wins & 28 nominations."           
[1881] "7 wins & 37 nominations."                                            
[1882] "Won 2 Oscars. Another 30 wins & 10 nominations."                     
[1883] "Won 2 Primetime Emmys. Another 8 wins & 18 nominations."             
[1884] "Nominated for 1 Golden Globe. Another 5 wins & 21 nominations."      
[1885] "Nominated for 2 Oscars. Another 27 wins & 46 nominations."           
[1886] "11 wins & 23 nominations."                                           
[1887] "Nominated for 2 Golden Globes. Another 12 wins & 30 nominations."    
[1888] "7 wins & 27 nominations."                                            
[1889] "Won 1 Oscar. Another 16 wins & 13 nominations."                      
[1890] "Nominated for 1 BAFTA Film Award. Another 17 wins & 19 nominations." 
[1891] "26 wins & 32 nominations."                                           
[1892] "Nominated for 5 Oscars. Another 12 wins & 62 nominations."           
[1893] "Won 3 Oscars. Another 129 wins & 121 nominations."                   
[1894] "Nominated for 2 Oscars. Another 6 wins & 31 nominations."            
[1895] "Won 3 Oscars. Another 26 wins & 42 nominations."                     
[1896] "1 win & 21 nominations."                                             
[1897] "Won 1 Oscar. Another 13 wins & 28 nominations."                      
[1898] "Nominated for 1 Oscar. Another 12 wins & 41 nominations."            
[1899] "Nominated for 4 Oscars. Another 13 wins & 52 nominations."           
[1900] "Nominated for 2 Oscars. Another 6 wins & 17 nominations."            
[1901] "Nominated for 6 Oscars. Another 38 wins & 119 nominations."          
[1902] "Nominated for 1 Oscar. Another 23 wins & 47 nominations."            
[1903] "Won 1 Oscar. Another 58 wins & 84 nominations."                      
[1904] "Nominated for 2 Oscars. Another 35 wins & 72 nominations."           
[1905] "Won 1 Oscar. Another 33 wins & 64 nominations."                      
[1906] "Nominated for 1 Oscar. Another 21 wins & 25 nominations."            
[1907] "Won 1 Oscar. Another 25 wins & 44 nominations."                      
[1908] "Nominated for 1 Oscar. Another 15 wins & 46 nominations."            
[1909] "Nominated for 1 Oscar. Another 17 wins & 12 nominations."            
[1910] "Nominated for 3 Oscars. Another 13 wins & 40 nominations."           
[1911] "Nominated for 1 Oscar. Another 15 wins & 66 nominations."            
[1912] "Nominated for 3 Oscars. Another 15 wins & 40 nominations."           
[1913] "Nominated for 1 Golden Globe. Another 21 wins & 40 nominations."     
[1914] "Nominated for 1 BAFTA Film Award. Another 2 wins & 6 nominations."   
[1915] "Nominated for 3 Oscars. Another 6 wins & 35 nominations."            
[1916] "Won 5 Oscars. Another 74 wins & 115 nominations."                    
[1917] "Nominated for 1 Oscar. Another 19 wins & 60 nominations."            
[1918] "Nominated for 2 Oscars. Another 17 wins & 33 nominations."           
[1919] "Nominated for 3 Oscars. Another 5 wins & 21 nominations."            
[1920] "Won 2 Oscars. Another 59 wins & 53 nominations."                     
[1921] "Won 2 Oscars. Another 52 wins & 51 nominations."                     
[1922] "54 wins & 7 nominations."                                            
[1923] "12 wins & 9 nominations."                                            
[1924] "Won 1 BAFTA Film Award. Another 5 wins & 11 nominations."            
[1925] "Nominated for 3 Oscars. Another 25 wins & 20 nominations."           
[1926] "Nominated for 1 Golden Globe. Another 28 wins & 13 nominations."     
[1927] "Nominated for 4 Oscars. Another 19 wins & 54 nominations."           
[1928] "Nominated for 1 Oscar. Another 9 wins & 25 nominations."             
[1929] "2 wins & 23 nominations."                                            
[1930] "Won 1 BAFTA Film Award. Another 28 wins & 31 nominations."           
[1931] "42 wins & 18 nominations."                                           
[1932] "Nominated for 1 Oscar. Another 5 wins & 14 nominations."             
[1933] "Nominated for 1 Oscar. Another 14 wins & 24 nominations."            
[1934] "Won 1 Oscar. Another 17 wins & 6 nominations."                       
[1935] "Won 1 Oscar. Another 41 wins & 120 nominations."                     
[1936] "Won 3 Oscars. Another 52 wins & 70 nominations."                     
[1937] "Nominated for 1 Oscar. Another 10 nominations."                      
[1938] "Won 2 Oscars. Another 114 wins & 127 nominations."                   
[1939] "Nominated for 2 Golden Globes. Another 2 wins & 10 nominations."     
[1940] "Won 4 Oscars. Another 33 wins & 63 nominations."                     
[1941] "Nominated for 1 Oscar. Another 10 wins & 45 nominations."            
[1942] "Nominated for 1 Oscar. Another 46 wins & 56 nominations."            
[1943] "Nominated for 1 Golden Globe. Another 28 wins & 50 nominations."     
[1944] "Nominated for 1 Golden Globe. Another 4 wins & 18 nominations."      
[1945] "4 wins & 17 nominations."                                            
[1946] "Nominated for 1 BAFTA Film Award. Another 6 wins & 28 nominations."  
[1947] "Nominated for 1 Oscar. Another 8 wins & 27 nominations."             
[1948] "Won 4 Oscars. Another 95 wins & 127 nominations."                    
[1949] "Nominated for 2 Oscars. Another 1 win & 20 nominations."             
[1950] "Nominated for 1 Oscar. Another 6 wins & 23 nominations."             
[1951] "Nominated for 2 Oscars. Another 4 wins & 24 nominations."            
[1952] "Nominated for 3 Oscars. Another 8 wins & 18 nominations."            
[1953] "Nominated for 1 Oscar. Another 7 wins & 17 nominations."             
[1954] "Nominated for 5 Oscars. Another 10 wins & 75 nominations."           
[1955] "Nominated for 2 Oscars. Another 2 wins & 25 nominations."            
[1956] "Nominated for 7 Oscars. Another 23 wins & 50 nominations."           
[1957] "Nominated for 1 Oscar. Another 14 wins & 37 nominations."            
[1958] "Nominated for 6 Oscars. Another 32 wins & 47 nominations."           
[1959] "21 wins & 18 nominations."                                           
[1960] "16 wins & 21 nominations."                                           
[1961] "Nominated for 2 Golden Globes. Another 4 wins & 15 nominations."     
[1962] "Nominated for 1 Oscar. Another 5 wins & 19 nominations."             
[1963] "Won 4 Oscars. Another 33 wins & 43 nominations."                     
[1964] "Nominated for 2 Oscars. Another 5 wins & 8 nominations."             
[1965] "Nominated for 7 Oscars. Another 20 wins & 41 nominations."           
[1966] "Won 1 Oscar. Another 9 wins & 27 nominations."                       
[1967] "Nominated for 2 Oscars. Another 14 wins & 22 nominations."           
[1968] "Nominated for 1 Oscar. Another 14 wins & 19 nominations."            
[1969] "Nominated for 2 Golden Globes. Another 3 wins & 2 nominations."      
[1970] "Nominated for 2 Oscars. Another 8 wins & 16 nominations."            
[1971] "Nominated for 1 Oscar. Another 4 wins & 18 nominations."             
[1972] "4 wins & 16 nominations."                                            
[1973] "Nominated for 1 Golden Globe. Another 2 wins & 9 nominations."       
[1974] "Nominated for 2 Oscars. Another 33 wins & 52 nominations."           
[1975] "Nominated for 1 Golden Globe. Another 5 wins & 30 nominations."      
[1976] "Won 1 Oscar. Another 35 wins & 31 nominations."                      
[1977] "Nominated for 1 Oscar. Another 9 wins & 33 nominations."             
[1978] "Won 9 Oscars. Another 49 wins & 66 nominations."                     
[1979] "Nominated for 1 Golden Globe. Another 7 wins & 11 nominations."      
[1980] "Won 1 Oscar. Another 8 wins & 11 nominations."                       
[1981] "Nominated for 1 Oscar. Another 8 wins & 20 nominations."             
[1982] "Nominated for 1 Oscar. Another 9 wins & 8 nominations."              
[1983] "Nominated for 1 Oscar. Another 1 win & 9 nominations."               
[1984] "Won 2 Oscars. Another 32 wins & 16 nominations."                     
[1985] "Nominated for 7 Oscars. Another 16 wins & 20 nominations."           
[1986] "Nominated for 1 Oscar. Another 6 wins & 17 nominations."             
[1987] "Won 2 Oscars. Another 29 wins & 27 nominations."                     
[1988] "Won 3 Oscars. Another 56 wins & 42 nominations."                     
[1989] "Nominated for 8 Oscars. Another 16 wins & 24 nominations."           
[1990] "Won 1 Oscar. Another 13 wins & 26 nominations."                      
[1991] "Won 1 Oscar. Another 11 wins & 28 nominations."                      
[1992] "Nominated for 4 Oscars. Another 9 wins & 19 nominations."            
[1993] "Won 1 Oscar. Another 20 wins & 36 nominations."                      
[1994] "Nominated for 2 Oscars. Another 11 wins & 18 nominations."           
[1995] "Won 3 Oscars. Another 12 wins & 15 nominations."                     
[1996] "Won 1 Oscar. Another 6 wins & 13 nominations."                       
[1997] "Nominated for 2 Golden Globes. Another 6 wins & 6 nominations."      
[1998] "Nominated for 3 Oscars. Another 25 wins & 22 nominations."           
[1999] "Nominated for 7 Oscars. Another 3 wins & 11 nominations."            
[2000] "Won 5 Oscars. Another 48 wins & 34 nominations."                     
[2001] "Nominated for 7 Oscars. Another 5 wins & 13 nominations."            
[2002] "Won 1 Oscar. Another 5 wins & 13 nominations."                       
[2003] "Won 9 Oscars. Another 43 wins & 15 nominations."                     
[2004] "Nominated for 1 Primetime Emmy. Another 1 win & 3 nominations."      
[2005] "Won 1 Oscar. Another 10 wins & 15 nominations."                      
[2006] "Won 3 Oscars. Another 21 wins & 23 nominations."                     
[2007] "Nominated for 11 Oscars. Another 13 wins & 13 nominations."          
[2008] "Won 2 Oscars. Another 19 wins & 26 nominations."                     
[2009] "Nominated for 2 Oscars. Another 1 win & 6 nominations."              
[2010] "Won 4 Oscars. Another 7 wins & 15 nominations."                      
[2011] "Nominated for 5 Oscars. Another 3 wins & 9 nominations."             
[2012] "Nominated for 6 Oscars. Another 13 wins & 5 nominations."            
[2013] "Nominated for 8 Oscars. Another 10 wins & 14 nominations."           
[2014] "Nominated for 2 Oscars. Another 6 nominations."                      
[2015] "Nominated for 2 Oscars. Another 7 wins & 4 nominations."             
[2016] "Nominated for 2 Oscars. Another 3 wins & 9 nominations."             
[2017] "Won 5 Oscars. Another 17 wins & 26 nominations."                     
[2018] "Nominated for 4 Oscars. Another 1 win & 7 nominations."              
[2019] "Nominated for 3 Oscars. Another 2 wins & 8 nominations."             
[2020] "Won 3 BAFTA Film Awards. Another 4 wins & 4 nominations."            
[2021] "Nominated for 3 Golden Globes. Another 2 wins & 2 nominations."      
[2022] "Nominated for 3 Golden Globes. Another 2 wins & 1 nomination."       
[2023] "Won 3 Oscars. Another 9 wins & 13 nominations."                      
[2024] "Won 6 Oscars. Another 10 wins & 20 nominations."                     
[2025] "Nominated for 3 Oscars. Another 14 wins & 13 nominations."           
[2026] "Won 7 Oscars. Another 10 wins & 6 nominations."                      
[2027] "Won 3 Oscars. Another 23 wins & 27 nominations."                     
[2028] "Won 5 Oscars. Another 17 wins & 10 nominations."                     
[2029] "Won 2 BAFTA Film Awards. Another 1 win & 2 nominations."             
[2030] "Nominated for 2 Oscars. Another 5 wins & 4 nominations."             
[2031] "Won 6 Oscars. Another 27 wins & 8 nominations."                      
[2032] "Won 5 Oscars. Another 12 wins & 13 nominations."                     
[2033] "Nominated for 5 Oscars. Another 1 win & 1 nomination."               
[2034] "Nominated for 2 Oscars. Another 2 wins & 5 nominations."             
[2035] "Won 2 Oscars. Another 6 wins & 6 nominations."                       
[2036] "Won 2 Oscars. Another 11 wins & 20 nominations."                     
[2037] "Won 5 Oscars. Another 19 wins & 8 nominations."                      
[2038] "Nominated for 8 Oscars. Another 11 wins & 15 nominations."           
[2039] "Won 7 Oscars. Another 23 wins & 7 nominations."                      
[2040] "Won 4 Oscars. Another 13 wins & 15 nominations."                     
[2041] "Won 7 Oscars. Another 13 wins & 2 nominations."                      
[2042] "Won 4 Oscars. Another 12 wins & 3 nominations."                      
print(summary(df6))
    Title              Awards              Gross          
 Length:39952       Length:39952       Min.   :0.000e+00  
 Class :character   Class :character   1st Qu.:5.000e+06  
 Mode  :character   Mode  :character   Median :3.006e+07  
                                       Mean   :9.057e+07  
                                       3rd Qu.:1.008e+08  
                                       Max.   :2.784e+09  
                                       NA's   :35442      
print(head(df6))
              Title  Awards Gross
1 39 Pounds of Love 3 wins.    NA
2              3:am     N/A    NA
3   500 Years Later 2 wins.    NA
4         5th World  1 win.    NA
5                90     N/A    NA
6  Abel Raises Cain 5 wins.    NA
awards = stri_extract_all(df6$Awards, regex="\\d+")
award_labels = stri_extract_all(df6$Awards, regex="win|won|nomin")
print(head(awards))
[[1]]
[1] "3"

[[2]]
[1] NA

[[3]]
[1] "2"

[[4]]
[1] "1"

[[5]]
[1] NA

[[6]]
[1] "5"
print(head(award_labels))
[[1]]
[1] "win"

[[2]]
[1] NA

[[3]]
[1] "win"

[[4]]
[1] "win"

[[5]]
[1] NA

[[6]]
[1] "win"
wins_list = c()
nominations_list = c()

for (i in 1:length(awards)){
    wins = 0
    nominations = 0
    for(j in 1:length(awards[[i]])){
        if(!is.na(award_labels[[i]][j]) & (award_labels[[i]][j] == 'wins' | award_labels[[i]][j] == 'win' | award_labels[[i]][j] == 'won')){
            wins = wins + as.numeric(awards[[i]][j])
        } else if (!is.na(award_labels[[i]][j]) & award_labels[[i]][j] == 'nomin'){
            nominations = nominations + as.numeric(awards[[i]][j])
        }
    }
    wins_list = c(wins_list, wins)
    nominations_list = c(nominations_list, nominations)
}

df$wins = wins_list
df$nominations = nominations_list

print(head(df[c('Awards', 'wins', 'nominations')], 20))
                    Awards wins nominations
1                  3 wins.    3           0
2                      N/A    0           0
3                  2 wins.    2           0
4                   1 win.    1           0
5                      N/A    0           0
6                  5 wins.    5           0
7                      N/A    0           0
8                      N/A    0           0
9                      N/A    0           0
10                     N/A    0           0
11                     N/A    0           0
12                     N/A    0           0
13 5 wins & 4 nominations.    5           4
14                     N/A    0           0
15   1 win & 1 nomination.    1           1
16                  1 win.    1           0
17                     N/A    0           0
18  2 wins & 1 nomination.    2           1
19                     N/A    0           0
20           1 nomination.    0           1

Q: How did you construct your conversion mechanism? How many rows had valid/non-zero wins or nominations?

A: To construct the conversion mechanism, I first used stri_extract_all to extract all numerical substrings. I then extracted all instances of “win, won, or nomin”. I then assigned the numerical to however it corresponded to the position of “win, won, or nomin”.

# TODO: Plot Gross revenue against wins and nominations
ggplot(df, aes(wins, Gross)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")
Warning: Removed 35442 rows containing non-finite values (stat_summary).

ggplot(df, aes(nominations, Gross)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")
Warning: Removed 35442 rows containing non-finite values (stat_summary).

Q: How does the gross revenue vary by number of awards won and nominations received?

A: Surprisingly, there does not seem to be much of a relationship between gross revenue and awards won.

7. Movie ratings from IMDb and Rotten Tomatoes

There are several variables that describe ratings, including IMDb ratings (imdbRating represents average user ratings and imdbVotes represents the number of user ratings), and multiple Rotten Tomatoes ratings (represented by several variables pre-fixed by tomato). Read up on such ratings on the web (for example rottentomatoes.com/about and www.imdb.com/help/show_leaf?votestopfaq).

Investigate the pairwise relationships between these different descriptors using graphs.

library(GGally)

# TODO: Illustrate how ratings from IMDb and Rotten Tomatoes are related
cols = c('imdbRating', 'imdbVotes', 'tomatoMeter', 'tomatoImage', 'tomatoRating', 'tomatoReviews',
         'tomatoFresh', 'tomatoRotten', 'tomatoConsensus', 'tomatoUserMeter', 'tomatoUserRating',
         'tomatoUserReviews')
df7 = df[cols]

print(summary(df7))
   imdbRating      imdbVotes        tomatoMeter     tomatoImage       
 Min.   :1.000   Min.   :      5   Min.   :  0.00   Length:39952      
 1st Qu.:5.600   1st Qu.:     60   1st Qu.: 35.00   Class :character  
 Median :6.400   Median :    211   Median : 63.00   Mode  :character  
 Mean   :6.234   Mean   :  12025   Mean   : 58.56                     
 3rd Qu.:7.100   3rd Qu.:   1294   3rd Qu.: 83.00                     
 Max.   :9.800   Max.   :1684836   Max.   :100.00                     
 NA's   :1134    NA's   :1172      NA's   :30539                      
  tomatoRating   tomatoReviews     tomatoFresh      tomatoRotten   
 Min.   :0.000   Min.   :  1.00   Min.   :  0.00   Min.   :  0.00  
 1st Qu.:4.800   1st Qu.: 11.00   1st Qu.:  5.00   1st Qu.:  3.00  
 Median :6.000   Median : 28.00   Median : 15.00   Median :  9.00  
 Mean   :5.875   Mean   : 57.47   Mean   : 34.32   Mean   : 23.14  
 3rd Qu.:7.000   3rd Qu.: 88.00   3rd Qu.: 43.00   3rd Qu.: 30.00  
 Max.   :9.800   Max.   :360.00   Max.   :343.00   Max.   :249.00  
 NA's   :30548   NA's   :30494    NA's   :30494    NA's   :30494   
 tomatoConsensus    tomatoUserMeter  tomatoUserRating tomatoUserReviews 
 Length:39952       Min.   :  0.00   Min.   :0.000    Min.   :       0  
 Class :character   1st Qu.: 38.00   1st Qu.:2.900    1st Qu.:      46  
 Mode  :character   Median : 57.00   Median :3.300    Median :     341  
                    Mean   : 55.51   Mean   :3.264    Mean   :   93049  
                    3rd Qu.: 75.00   3rd Qu.:3.600    3rd Qu.:    4947  
                    Max.   :100.00   Max.   :5.000    Max.   :35794556  
                    NA's   :21936    NA's   :21852    NA's   :15980     
print(head(df7))
  imdbRating imdbVotes tomatoMeter tomatoImage tomatoRating tomatoReviews
1        6.8       234          NA         N/A           NA            NA
2        8.2        13          NA         N/A           NA            NA
3        7.0       161          NA         N/A           NA            NA
4        6.4        31          NA         N/A           NA            NA
5        8.3        18          NA         N/A           NA            NA
6        7.5       197          NA         N/A           NA            NA
  tomatoFresh tomatoRotten tomatoConsensus tomatoUserMeter
1          NA           NA             N/A              NA
2          NA           NA             N/A              NA
3          NA           NA             N/A              88
4          NA           NA             N/A              NA
5          NA           NA             N/A              NA
6          NA           NA             N/A              60
  tomatoUserRating tomatoUserReviews
1               NA                50
2               NA                NA
3              4.2               341
4               NA                 8
5               NA                NA
6              4.0                67
ggpairs(df7, columns=c('imdbRating', 'tomatoMeter', 'tomatoRating'), aes())
Warning: Removed 1134 rows containing non-finite values (stat_density).
Warning in (function (data, mapping, alignPercent = 0.6, method =
"pearson", : Removed 30541 rows containing missing values
Warning in (function (data, mapping, alignPercent = 0.6, method =
"pearson", : Removed 30550 rows containing missing values
Warning: Removed 30541 rows containing missing values (geom_point).
Warning: Removed 30539 rows containing non-finite values (stat_density).
Warning in (function (data, mapping, alignPercent = 0.6, method =
"pearson", : Removed 30548 rows containing missing values
Warning: Removed 30550 rows containing missing values (geom_point).
Warning: Removed 30548 rows containing missing values (geom_point).
Warning: Removed 30548 rows containing non-finite values (stat_density).

Q: Comment on the similarities and differences between the user ratings of IMDb and the critics ratings of Rotten Tomatoes.

A: First off, it’s interesting to note that we have significantly more data of IMDB ratings than Rotten tomatoes. In our data, we have about 1100 movies with missing IMDB ratings. However, we have close to 30,000 movies with missing Rotten Tomatoes ratings.

In the plot above we plot the ggpairs of imdbRating, tomatoMeter, and tomatoRating. Notice that the correlation between tomatoMeter and tomatoRating is very high, at 0.94. This makes sense as it’s from the same source. The correlation between tomatoMeter and imdbRating is a bit lower, at around 0.746 vs 0.794 which is the correlation between tomatoRating and imdbRating.

Looking at the summary of the variables as well, it looks like imdbRating and tomatoRating are rated on similar scales. imdbRating looks like it’s rated on a scale of 1 to 10, while tomatoRating looks like it’s rated on a scale of 0 to 9.8. However, tomatoMeter looks like it’s scaled from 0 to 100.

8. Ratings and awards

These ratings typically reflect the general appeal of the movie to the public or gather opinions from a larger body of critics. Whereas awards are given by professional societies that may evaluate a movie on specific attributes, such as artistic performance, screenplay, sound design, etc.

Study the relationship between ratings and awards using graphs (awards here refers to wins and/or nominations).

# TODO: Show how ratings and awards are related
ggplot(df, aes(imdbRating, wins)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")
Warning: Removed 1134 rows containing non-finite values (stat_summary).

ggplot(df, aes(imdbRating, nominations)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")
Warning: Removed 1134 rows containing non-finite values (stat_summary).

ggplot(df, aes(tomatoRating, wins)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")
Warning: Removed 30548 rows containing non-finite values (stat_summary).

ggplot(df, aes(tomatoRating, nominations)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")
Warning: Removed 30548 rows containing non-finite values (stat_summary).

mod1 = lm(wins ~ imdbRating, data = df)
summary(mod1)

Call:
lm(formula = wins ~ imdbRating, data = df)

Residuals:
   Min     1Q Median     3Q    Max 
-1.938 -0.883 -0.531  0.067 52.695 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.439096   0.054933  -26.20   <2e-16 ***
imdbRating   0.351807   0.008653   40.66   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.047 on 38816 degrees of freedom
  (1134 observations deleted due to missingness)
Multiple R-squared:  0.04084,   Adjusted R-squared:  0.04082 
F-statistic:  1653 on 1 and 38816 DF,  p-value: < 2.2e-16
mod1 = lm(nominations ~ imdbRating, data = df)
summary(mod1)

Call:
lm(formula = nominations ~ imdbRating, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
 -4.496  -1.947  -1.156   0.019 229.822 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -3.94098    0.16743  -23.54   <2e-16 ***
imdbRating   0.87885    0.02637   33.32   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 6.238 on 38816 degrees of freedom
  (1134 observations deleted due to missingness)
Multiple R-squared:  0.02781,   Adjusted R-squared:  0.02778 
F-statistic:  1110 on 1 and 38816 DF,  p-value: < 2.2e-16
mod1 = lm(wins ~ tomatoRating, data = df)
summary(mod1)

Call:
lm(formula = wins ~ tomatoRating, data = df)

Residuals:
   Min     1Q Median     3Q    Max 
-3.348 -1.445 -0.744  0.305 51.353 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -1.30926    0.12455  -10.51   <2e-16 ***
tomatoRating  0.50076    0.02052   24.41   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 3.043 on 9402 degrees of freedom
  (30548 observations deleted due to missingness)
Multiple R-squared:  0.05958,   Adjusted R-squared:  0.05948 
F-statistic: 595.7 on 1 and 9402 DF,  p-value: < 2.2e-16
mod1 = lm(nominations ~ tomatoRating, data = df)
summary(mod1)

Call:
lm(formula = nominations ~ tomatoRating, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-14.050  -4.886  -1.832   1.584 220.595 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -8.74148    0.46064  -18.98   <2e-16 ***
tomatoRating  2.34962    0.07588   30.96   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 11.25 on 9402 degrees of freedom
  (30548 observations deleted due to missingness)
Multiple R-squared:  0.09253,   Adjusted R-squared:  0.09244 
F-statistic: 958.7 on 1 and 9402 DF,  p-value: < 2.2e-16

Q: How good are these ratings in terms of predicting the success of a movie in winning awards or nominations? Is there a high correlation between two variables?

A: One extremely interesting trend from the graph above is that for both imdbRatings and tomatoRatings generally there is a positive correlation between ratings and both wins and nominations. But very interestingly enough, the relationship is actually not monotonic. In the extremely upper regions of ratings, the number of wins and nominations actually decrease!

I fit a linear regression between wins and nominations against ratings field. Notice that in each regression the coefficient is significant, which means that there is a significant positive relationship between rating and nominations and wins. However, the R^2 is relatively low, so the correlation is relatively low.

9. Expected insights

Come up with two new insights (backed up by data and graphs) that is expected. Here “new” means insights that are not an immediate consequence of one of the above tasks. You may use any of the columns already explored above or a different one in the dataset, such as Title, Actors, etc.

# TODO: Find and illustrate two expected insights
df = cbind(df, mtabulate(strsplit(df$Country, ", ")))
print(summary(df))
    Title                Year         Rated              Released         
 Length:39952       Min.   :1888   Length:39952       Min.   :1893-05-09  
 Class :character   1st Qu.:1961   Class :character   1st Qu.:1959-02-04  
 Mode  :character   Median :1989   Mode  :character   Median :1989-10-06  
                    Mean   :1981                      Mean   :1980-12-02  
                    3rd Qu.:2001                      3rd Qu.:2001-11-07  
                    Max.   :2018                      Max.   :2018-08-01  
                                                      NA's   :4949        
    Runtime         Director            Writer             Actors         
 Min.   :  1.00   Length:39952       Length:39952       Length:39952      
 1st Qu.: 72.00   Class :character   Class :character   Class :character  
 Median : 90.00   Mode  :character   Mode  :character   Mode  :character  
 Mean   : 81.78                                                           
 3rd Qu.:101.00                                                           
 Max.   :873.00                                                           
 NA's   :751                                                              
     Plot             Language           Country         
 Length:39952       Length:39952       Length:39952      
 Class :character   Class :character   Class :character  
 Mode  :character   Mode  :character   Mode  :character  
                                                         
                                                         
                                                         
                                                         
    Awards             Poster           Metascore           imdbRating   
 Length:39952       Length:39952       Length:39952       Min.   :1.000  
 Class :character   Class :character   Class :character   1st Qu.:5.600  
 Mode  :character   Mode  :character   Mode  :character   Median :6.400  
                                                          Mean   :6.234  
                                                          3rd Qu.:7.100  
                                                          Max.   :9.800  
                                                          NA's   :1134   
   imdbVotes          imdbID              Type            tomatoMeter    
 Min.   :      5   Length:39952       Length:39952       Min.   :  0.00  
 1st Qu.:     60   Class :character   Class :character   1st Qu.: 35.00  
 Median :    211   Mode  :character   Mode  :character   Median : 63.00  
 Mean   :  12025                                         Mean   : 58.56  
 3rd Qu.:   1294                                         3rd Qu.: 83.00  
 Max.   :1684836                                         Max.   :100.00  
 NA's   :1172                                            NA's   :30539   
 tomatoImage         tomatoRating   tomatoReviews     tomatoFresh    
 Length:39952       Min.   :0.000   Min.   :  1.00   Min.   :  0.00  
 Class :character   1st Qu.:4.800   1st Qu.: 11.00   1st Qu.:  5.00  
 Mode  :character   Median :6.000   Median : 28.00   Median : 15.00  
                    Mean   :5.875   Mean   : 57.47   Mean   : 34.32  
                    3rd Qu.:7.000   3rd Qu.: 88.00   3rd Qu.: 43.00  
                    Max.   :9.800   Max.   :360.00   Max.   :343.00  
                    NA's   :30548   NA's   :30494    NA's   :30494   
  tomatoRotten    tomatoConsensus    tomatoUserMeter  tomatoUserRating
 Min.   :  0.00   Length:39952       Min.   :  0.00   Min.   :0.000   
 1st Qu.:  3.00   Class :character   1st Qu.: 38.00   1st Qu.:2.900   
 Median :  9.00   Mode  :character   Median : 57.00   Median :3.300   
 Mean   : 23.14                      Mean   : 55.51   Mean   :3.264   
 3rd Qu.: 30.00                      3rd Qu.: 75.00   3rd Qu.:3.600   
 Max.   :249.00                      Max.   :100.00   Max.   :5.000   
 NA's   :30494                       NA's   :21936    NA's   :21852   
 tomatoUserReviews   tomatoURL              DVD            
 Min.   :       0   Length:39952       Min.   :1933-06-30  
 1st Qu.:      46   Class :character   1st Qu.:2000-12-19  
 Median :     341   Mode  :character   Median :2004-04-13  
 Mean   :   93049                      Mean   :2004-01-18  
 3rd Qu.:    4947                      3rd Qu.:2007-02-20  
 Max.   :35794556                      Max.   :2016-10-04  
 NA's   :15980                         NA's   :23185       
  BoxOffice          Production          Website         
 Length:39952       Length:39952       Length:39952      
 Class :character   Class :character   Class :character  
 Mode  :character   Mode  :character   Mode  :character  
                                                         
                                                         
                                                         
                                                         
   Response             Budget          Domestic_Gross     
 Length:39952       Min.   :     1100   Min.   :        0  
 Class :character   1st Qu.:  5000000   1st Qu.:  2453542  
 Mode  :character   Median : 18000000   Median : 19539834  
                    Mean   : 31952913   Mean   : 42934963  
                    3rd Qu.: 40000000   3rd Qu.: 55070326  
                    Max.   :425000000   Max.   :760507625  
                    NA's   :35442       NA's   :35442      
     Gross                Date           Action           Adult        
 Min.   :0.000e+00   Min.   :1915    Min.   :0.0000   Min.   :0.00000  
 1st Qu.:5.000e+06   1st Qu.:1999    1st Qu.:0.0000   1st Qu.:0.00000  
 Median :3.006e+07   Median :2005    Median :0.0000   Median :0.00000  
 Mean   :9.057e+07   Mean   :2003    Mean   :0.1104   Mean   :0.01056  
 3rd Qu.:1.008e+08   3rd Qu.:2011    3rd Qu.:0.0000   3rd Qu.:0.00000  
 Max.   :2.784e+09   Max.   :2017    Max.   :1.0000   Max.   :1.00000  
 NA's   :35442       NA's   :35442                                     
   Adventure         Animation         Biography           Comedy      
 Min.   :0.00000   Min.   :0.00000   Min.   :0.00000   Min.   :0.0000  
 1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.0000  
 Median :0.00000   Median :0.00000   Median :0.00000   Median :0.0000  
 Mean   :0.07324   Mean   :0.06976   Mean   :0.02768   Mean   :0.3212  
 3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:1.0000  
 Max.   :1.00000   Max.   :1.00000   Max.   :1.00000   Max.   :1.0000  
                                                                       
     Crime         Documentary          Drama            Family       
 Min.   :0.0000   Min.   :0.00000   Min.   :0.0000   Min.   :0.00000  
 1st Qu.:0.0000   1st Qu.:0.00000   1st Qu.:0.0000   1st Qu.:0.00000  
 Median :0.0000   Median :0.00000   Median :0.0000   Median :0.00000  
 Mean   :0.1014   Mean   :0.07624   Mean   :0.3963   Mean   :0.06638  
 3rd Qu.:0.0000   3rd Qu.:0.00000   3rd Qu.:1.0000   3rd Qu.:0.00000  
 Max.   :1.0000   Max.   :1.00000   Max.   :1.0000   Max.   :1.00000  
                                                                      
    Fantasy          Film-Noir          Game-Show           History       
 Min.   :0.00000   Min.   :0.000000   Min.   :0.00e+00   Min.   :0.00000  
 1st Qu.:0.00000   1st Qu.:0.000000   1st Qu.:0.00e+00   1st Qu.:0.00000  
 Median :0.00000   Median :0.000000   Median :0.00e+00   Median :0.00000  
 Mean   :0.03502   Mean   :0.008811   Mean   :5.01e-05   Mean   :0.02075  
 3rd Qu.:0.00000   3rd Qu.:0.000000   3rd Qu.:0.00e+00   3rd Qu.:0.00000  
 Max.   :1.00000   Max.   :1.000000   Max.   :1.00e+00   Max.   :1.00000  
                                                                          
     Horror            Music            Musical           Mystery       
 Min.   :0.00000   Min.   :0.00000   Min.   :0.00000   Min.   :0.00000  
 1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.00000  
 Median :0.00000   Median :0.00000   Median :0.00000   Median :0.00000  
 Mean   :0.06781   Mean   :0.02961   Mean   :0.03469   Mean   :0.04097  
 3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.00000  
 Max.   :1.00000   Max.   :1.00000   Max.   :1.00000   Max.   :1.00000  
                                                                        
      N/A               News             Reality-TV       
 Min.   :0.00000   Min.   :0.0000000   Min.   :0.0000000  
 1st Qu.:0.00000   1st Qu.:0.0000000   1st Qu.:0.0000000  
 Median :0.00000   Median :0.0000000   Median :0.0000000  
 Mean   :0.02468   Mean   :0.0005006   Mean   :0.0001502  
 3rd Qu.:0.00000   3rd Qu.:0.0000000   3rd Qu.:0.0000000  
 Max.   :1.00000   Max.   :1.0000000   Max.   :1.0000000  
                                                          
    Romance           Sci-Fi            Short            Sport        
 Min.   :0.0000   Min.   :0.00000   Min.   :0.0000   Min.   :0.00000  
 1st Qu.:0.0000   1st Qu.:0.00000   1st Qu.:0.0000   1st Qu.:0.00000  
 Median :0.0000   Median :0.00000   Median :0.0000   Median :0.00000  
 Mean   :0.1243   Mean   :0.04052   Mean   :0.1631   Mean   :0.01314  
 3rd Qu.:0.0000   3rd Qu.:0.00000   3rd Qu.:0.0000   3rd Qu.:0.00000  
 Max.   :1.0000   Max.   :1.00000   Max.   :1.0000   Max.   :1.00000  
                                                                      
   Talk-Show            Thriller            War             Western       
 Min.   :0.0000000   Min.   :0.00000   Min.   :0.00000   Min.   :0.00000  
 1st Qu.:0.0000000   1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.00000  
 Median :0.0000000   Median :0.00000   Median :0.00000   Median :0.00000  
 Mean   :0.0001001   Mean   :0.08438   Mean   :0.02676   Mean   :0.03289  
 3rd Qu.:0.0000000   3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.00000  
 Max.   :1.0000000   Max.   :1.00000   Max.   :1.00000   Max.   :1.00000  
                                                                          
 released_month        wins          nominations       Afghanistan      
 Min.   : 1.000   Min.   : 0.0000   Min.   :  0.000   Min.   :0.00e+00  
 1st Qu.: 4.000   1st Qu.: 0.0000   1st Qu.:  0.000   1st Qu.:0.00e+00  
 Median : 7.000   Median : 0.0000   Median :  0.000   Median :0.00e+00  
 Mean   : 6.624   Mean   : 0.7332   Mean   :  1.494   Mean   :5.01e-05  
 3rd Qu.:10.000   3rd Qu.: 1.0000   3rd Qu.:  1.000   3rd Qu.:0.00e+00  
 Max.   :12.000   Max.   :54.0000   Max.   :233.000   Max.   :1.00e+00  
 NA's   :4949                                                           
    Albania            Algeria              Angola        
 Min.   :0.00e+00   Min.   :0.0000000   Min.   :0.00e+00  
 1st Qu.:0.00e+00   1st Qu.:0.0000000   1st Qu.:0.00e+00  
 Median :0.00e+00   Median :0.0000000   Median :0.00e+00  
 Mean   :7.51e-05   Mean   :0.0003004   Mean   :5.01e-05  
 3rd Qu.:0.00e+00   3rd Qu.:0.0000000   3rd Qu.:0.00e+00  
 Max.   :1.00e+00   Max.   :1.0000000   Max.   :1.00e+00  
                                                          
   Antarctica         Argentina           Armenia         
 Min.   :0.00e+00   Min.   :0.000000   Min.   :0.0000000  
 1st Qu.:0.00e+00   1st Qu.:0.000000   1st Qu.:0.0000000  
 Median :0.00e+00   Median :0.000000   Median :0.0000000  
 Mean   :5.01e-05   Mean   :0.005682   Mean   :0.0001752  
 3rd Qu.:0.00e+00   3rd Qu.:0.000000   3rd Qu.:0.0000000  
 Max.   :1.00e+00   Max.   :1.000000   Max.   :1.0000000  
                                                          
     Aruba            Australia          Austria        
 Min.   :0.00e+00   Min.   :0.00000   Min.   :0.000000  
 1st Qu.:0.00e+00   1st Qu.:0.00000   1st Qu.:0.000000  
 Median :0.00e+00   Median :0.00000   Median :0.000000  
 Mean   :5.01e-05   Mean   :0.01685   Mean   :0.004806  
 3rd Qu.:0.00e+00   3rd Qu.:0.00000   3rd Qu.:0.000000  
 Max.   :1.00e+00   Max.   :1.00000   Max.   :1.000000  
                                                        
   Azerbaijan          Bahamas            Bangladesh      
 Min.   :0.00e+00   Min.   :0.0000000   Min.   :0.00e+00  
 1st Qu.:0.00e+00   1st Qu.:0.0000000   1st Qu.:0.00e+00  
 Median :0.00e+00   Median :0.0000000   Median :0.00e+00  
 Mean   :5.01e-05   Mean   :0.0001001   Mean   :7.51e-05  
 3rd Qu.:0.00e+00   3rd Qu.:0.0000000   3rd Qu.:0.00e+00  
 Max.   :1.00e+00   Max.   :1.0000000   Max.   :1.00e+00  
                                                          
    Belarus             Belgium             Benin        
 Min.   :0.0000000   Min.   :0.000000   Min.   :0.0e+00  
 1st Qu.:0.0000000   1st Qu.:0.000000   1st Qu.:0.0e+00  
 Median :0.0000000   Median :0.000000   Median :0.0e+00  
 Mean   :0.0001252   Mean   :0.006758   Mean   :2.5e-05  
 3rd Qu.:0.0000000   3rd Qu.:0.000000   3rd Qu.:0.0e+00  
 Max.   :1.0000000   Max.   :1.000000   Max.   :1.0e+00  
                                                         
    Bermuda           Bolivia        Bosnia and Herzegovina
 Min.   :0.0e+00   Min.   :0.0e+00   Min.   :0.0000000     
 1st Qu.:0.0e+00   1st Qu.:0.0e+00   1st Qu.:0.0000000     
 Median :0.0e+00   Median :0.0e+00   Median :0.0000000     
 Mean   :2.5e-05   Mean   :2.5e-05   Mean   :0.0002503     
 3rd Qu.:0.0e+00   3rd Qu.:0.0e+00   3rd Qu.:0.0000000     
 Max.   :1.0e+00   Max.   :1.0e+00   Max.   :1.0000000     
                                                           
     Brazil            Bulgaria         Burkina Faso      
 Min.   :0.000000   Min.   :0.000000   Min.   :0.0000000  
 1st Qu.:0.000000   1st Qu.:0.000000   1st Qu.:0.0000000  
 Median :0.000000   Median :0.000000   Median :0.0000000  
 Mean   :0.006132   Mean   :0.003004   Mean   :0.0002753  
 3rd Qu.:0.000000   3rd Qu.:0.000000   3rd Qu.:0.0000000  
 Max.   :1.000000   Max.   :1.000000   Max.   :1.0000000  
                                                          
    Cambodia          Cameroon            Canada             Chad         
 Min.   :0.0e+00   Min.   :0.00e+00   Min.   :0.00000   Min.   :0.00e+00  
 1st Qu.:0.0e+00   1st Qu.:0.00e+00   1st Qu.:0.00000   1st Qu.:0.00e+00  
 Median :0.0e+00   Median :0.00e+00   Median :0.00000   Median :0.00e+00  
 Mean   :2.5e-05   Mean   :7.51e-05   Mean   :0.04936   Mean   :7.51e-05  
 3rd Qu.:0.0e+00   3rd Qu.:0.00e+00   3rd Qu.:0.00000   3rd Qu.:0.00e+00  
 Max.   :1.0e+00   Max.   :1.00e+00   Max.   :1.00000   Max.   :1.00e+00  
                                                                          
     Chile              China             Colombia        
 Min.   :0.000000   Min.   :0.000000   Min.   :0.0000000  
 1st Qu.:0.000000   1st Qu.:0.000000   1st Qu.:0.0000000  
 Median :0.000000   Median :0.000000   Median :0.0000000  
 Mean   :0.001201   Mean   :0.003529   Mean   :0.0004005  
 3rd Qu.:0.000000   3rd Qu.:0.000000   3rd Qu.:0.0000000  
 Max.   :1.000000   Max.   :1.000000   Max.   :1.0000000  
                                                          
   Costa Rica       Côte d'Ivoire        Croatia         
 Min.   :0.00e+00   Min.   :0.0e+00   Min.   :0.0000000  
 1st Qu.:0.00e+00   1st Qu.:0.0e+00   1st Qu.:0.0000000  
 Median :0.00e+00   Median :0.0e+00   Median :0.0000000  
 Mean   :7.51e-05   Mean   :2.5e-05   Mean   :0.0006508  
 3rd Qu.:0.00e+00   3rd Qu.:0.0e+00   3rd Qu.:0.0000000  
 Max.   :1.00e+00   Max.   :1.0e+00   Max.   :1.0000000  
                                                         
      Cuba              Cyprus          Czech Republic    
 Min.   :0.000000   Min.   :0.0000000   Min.   :0.000000  
 1st Qu.:0.000000   1st Qu.:0.0000000   1st Qu.:0.000000  
 Median :0.000000   Median :0.0000000   Median :0.000000  
 Mean   :0.001076   Mean   :0.0001252   Mean   :0.002228  
 3rd Qu.:0.000000   3rd Qu.:0.0000000   3rd Qu.:0.000000  
 Max.   :1.000000   Max.   :1.0000000   Max.   :1.000000  
                                                          
 Czechoslovakia        Denmark         Dominican Republic 
 Min.   :0.000000   Min.   :0.000000   Min.   :0.0000000  
 1st Qu.:0.000000   1st Qu.:0.000000   1st Qu.:0.0000000  
 Median :0.000000   Median :0.000000   Median :0.0000000  
 Mean   :0.002278   Mean   :0.009536   Mean   :0.0001752  
 3rd Qu.:0.000000   3rd Qu.:0.000000   3rd Qu.:0.0000000  
 Max.   :1.000000   Max.   :1.000000   Max.   :1.0000000  
                                                          
  East Germany          Ecuador             Egypt          
 Min.   :0.0000000   Min.   :0.00e+00   Min.   :0.0000000  
 1st Qu.:0.0000000   1st Qu.:0.00e+00   1st Qu.:0.0000000  
 Median :0.0000000   Median :0.00e+00   Median :0.0000000  
 Mean   :0.0009762   Mean   :5.01e-05   Mean   :0.0007259  
 3rd Qu.:0.0000000   3rd Qu.:0.00e+00   3rd Qu.:0.0000000  
 Max.   :1.0000000   Max.   :1.00e+00   Max.   :1.0000000  
                                                           
    Estonia             Ethiopia       Federal Republic of Yugoslavia
 Min.   :0.0000000   Min.   :0.0e+00   Min.   :0.0000000             
 1st Qu.:0.0000000   1st Qu.:0.0e+00   1st Qu.:0.0000000             
 Median :0.0000000   Median :0.0e+00   Median :0.0000000             
 Mean   :0.0003755   Mean   :2.5e-05   Mean   :0.0005256             
 3rd Qu.:0.0000000   3rd Qu.:0.0e+00   3rd Qu.:0.0000000             
 Max.   :1.0000000   Max.   :1.0e+00   Max.   :1.0000000             
                                                                     
      Fiji            Finland             France            Gabon        
 Min.   :0.0e+00   Min.   :0.000000   Min.   :0.00000   Min.   :0.0e+00  
 1st Qu.:0.0e+00   1st Qu.:0.000000   1st Qu.:0.00000   1st Qu.:0.0e+00  
 Median :0.0e+00   Median :0.000000   Median :0.00000   Median :0.0e+00  
 Mean   :2.5e-05   Mean   :0.005657   Mean   :0.05724   Mean   :2.5e-05  
 3rd Qu.:0.0e+00   3rd Qu.:0.000000   3rd Qu.:0.00000   3rd Qu.:0.0e+00  
 Max.   :1.0e+00   Max.   :1.000000   Max.   :1.00000   Max.   :1.0e+00  
                                                                         
    Georgia             Germany            Ghana          
 Min.   :0.0000000   Min.   :0.00000   Min.   :0.0000000  
 1st Qu.:0.0000000   1st Qu.:0.00000   1st Qu.:0.0000000  
 Median :0.0000000   Median :0.00000   Median :0.0000000  
 Mean   :0.0002253   Mean   :0.03654   Mean   :0.0001252  
 3rd Qu.:0.0000000   3rd Qu.:0.00000   3rd Qu.:0.0000000  
 Max.   :1.0000000   Max.   :1.00000   Max.   :1.0000000  
                                                          
   Gibraltar           Greece           Guatemala            Guinea       
 Min.   :0.0e+00   Min.   :0.000000   Min.   :0.00e+00   Min.   :0.0e+00  
 1st Qu.:0.0e+00   1st Qu.:0.000000   1st Qu.:0.00e+00   1st Qu.:0.0e+00  
 Median :0.0e+00   Median :0.000000   Median :0.00e+00   Median :0.0e+00  
 Mean   :2.5e-05   Mean   :0.003279   Mean   :5.01e-05   Mean   :2.5e-05  
 3rd Qu.:0.0e+00   3rd Qu.:0.000000   3rd Qu.:0.00e+00   3rd Qu.:0.0e+00  
 Max.   :1.0e+00   Max.   :1.000000   Max.   :1.00e+00   Max.   :1.0e+00  
                                                                          
 Guinea-Bissau          Guyana            Haiti          
 Min.   :0.00e+00   Min.   :0.0e+00   Min.   :0.0000000  
 1st Qu.:0.00e+00   1st Qu.:0.0e+00   1st Qu.:0.0000000  
 Median :0.00e+00   Median :0.0e+00   Median :0.0000000  
 Mean   :7.51e-05   Mean   :2.5e-05   Mean   :0.0001001  
 3rd Qu.:0.00e+00   3rd Qu.:0.0e+00   3rd Qu.:0.0000000  
 Max.   :1.00e+00   Max.   :1.0e+00   Max.   :1.0000000  
                                                         
    Honduras         Hong Kong           Hungary        
 Min.   :0.0e+00   Min.   :0.000000   Min.   :0.000000  
 1st Qu.:0.0e+00   1st Qu.:0.000000   1st Qu.:0.000000  
 Median :0.0e+00   Median :0.000000   Median :0.000000  
 Mean   :2.5e-05   Mean   :0.009912   Mean   :0.002929  
 3rd Qu.:0.0e+00   3rd Qu.:0.000000   3rd Qu.:0.000000  
 Max.   :1.0e+00   Max.   :1.000000   Max.   :1.000000  
                                                        
    Iceland             India           Indonesia       
 Min.   :0.000000   Min.   :0.00000   Min.   :0.000000  
 1st Qu.:0.000000   1st Qu.:0.00000   1st Qu.:0.000000  
 Median :0.000000   Median :0.00000   Median :0.000000  
 Mean   :0.000801   Mean   :0.01815   Mean   :0.001026  
 3rd Qu.:0.000000   3rd Qu.:0.00000   3rd Qu.:0.000000  
 Max.   :1.000000   Max.   :1.00000   Max.   :1.000000  
                                                        
      Iran               Iraq             Ireland        
 Min.   :0.000000   Min.   :0.00e+00   Min.   :0.000000  
 1st Qu.:0.000000   1st Qu.:0.00e+00   1st Qu.:0.000000  
 Median :0.000000   Median :0.00e+00   Median :0.000000  
 Mean   :0.001902   Mean   :7.51e-05   Mean   :0.004906  
 3rd Qu.:0.000000   3rd Qu.:0.00e+00   3rd Qu.:0.000000  
 Max.   :1.000000   Max.   :1.00e+00   Max.   :1.000000  
                                                         
  Isle Of Man           Israel             Italy        
 Min.   :0.00e+00   Min.   :0.000000   Min.   :0.00000  
 1st Qu.:0.00e+00   1st Qu.:0.000000   1st Qu.:0.00000  
 Median :0.00e+00   Median :0.000000   Median :0.00000  
 Mean   :7.51e-05   Mean   :0.004355   Mean   :0.03094  
 3rd Qu.:0.00e+00   3rd Qu.:0.000000   3rd Qu.:0.00000  
 Max.   :1.00e+00   Max.   :1.000000   Max.   :1.00000  
                                                        
    Jamaica              Japan            Jordan        
 Min.   :0.0000000   Min.   :0.0000   Min.   :0.00e+00  
 1st Qu.:0.0000000   1st Qu.:0.0000   1st Qu.:0.00e+00  
 Median :0.0000000   Median :0.0000   Median :0.00e+00  
 Mean   :0.0002002   Mean   :0.0195   Mean   :7.51e-05  
 3rd Qu.:0.0000000   3rd Qu.:0.0000   3rd Qu.:0.00e+00  
 Max.   :1.0000000   Max.   :1.0000   Max.   :1.00e+00  
                                                        
   Kazakhstan            Kenya               Korea        
 Min.   :0.0000000   Min.   :0.0000000   Min.   :0.0e+00  
 1st Qu.:0.0000000   1st Qu.:0.0000000   1st Qu.:0.0e+00  
 Median :0.0000000   Median :0.0000000   Median :0.0e+00  
 Mean   :0.0002503   Mean   :0.0001001   Mean   :2.5e-05  
 3rd Qu.:0.0000000   3rd Qu.:0.0000000   3rd Qu.:0.0e+00  
 Max.   :1.0000000   Max.   :1.0000000   Max.   :1.0e+00  
                                                          
   Kyrgyzstan           Latvia             Lebanon         
 Min.   :0.00e+00   Min.   :0.0000000   Min.   :0.0000000  
 1st Qu.:0.00e+00   1st Qu.:0.0000000   1st Qu.:0.0000000  
 Median :0.00e+00   Median :0.0000000   Median :0.0000000  
 Mean   :5.01e-05   Mean   :0.0001502   Mean   :0.0002002  
 3rd Qu.:0.00e+00   3rd Qu.:0.0000000   3rd Qu.:0.0000000  
 Max.   :1.00e+00   Max.   :1.0000000   Max.   :1.0000000  
                                                           
     Libya         Liechtenstein         Lithuania        
 Min.   :0.0e+00   Min.   :0.0000000   Min.   :0.0000000  
 1st Qu.:0.0e+00   1st Qu.:0.0000000   1st Qu.:0.0000000  
 Median :0.0e+00   Median :0.0000000   Median :0.0000000  
 Mean   :2.5e-05   Mean   :0.0002002   Mean   :0.0001502  
 3rd Qu.:0.0e+00   3rd Qu.:0.0000000   3rd Qu.:0.0000000  
 Max.   :1.0e+00   Max.   :1.0000000   Max.   :1.0000000  
                                                          
   Luxembourg          Malaysia              Mali          
 Min.   :0.000000   Min.   :0.0000000   Min.   :0.0000000  
 1st Qu.:0.000000   1st Qu.:0.0000000   1st Qu.:0.0000000  
 Median :0.000000   Median :0.0000000   Median :0.0000000  
 Mean   :0.001402   Mean   :0.0004756   Mean   :0.0001752  
 3rd Qu.:0.000000   3rd Qu.:0.0000000   3rd Qu.:0.0000000  
 Max.   :1.000000   Max.   :1.0000000   Max.   :1.0000000  
                                                           
     Malta            Mauritania           Mexico        
 Min.   :0.00e+00   Min.   :0.00e+00   Min.   :0.000000  
 1st Qu.:0.00e+00   1st Qu.:0.00e+00   1st Qu.:0.000000  
 Median :0.00e+00   Median :0.00e+00   Median :0.000000  
 Mean   :7.51e-05   Mean   :5.01e-05   Mean   :0.007359  
 3rd Qu.:0.00e+00   3rd Qu.:0.00e+00   3rd Qu.:0.000000  
 Max.   :1.00e+00   Max.   :1.00e+00   Max.   :1.000000  
                                                         
   Micronesia         Moldova            Monaco            Mongolia      
 Min.   :0.0e+00   Min.   :0.0e+00   Min.   :0.00e+00   Min.   :0.0e+00  
 1st Qu.:0.0e+00   1st Qu.:0.0e+00   1st Qu.:0.00e+00   1st Qu.:0.0e+00  
 Median :0.0e+00   Median :0.0e+00   Median :0.00e+00   Median :0.0e+00  
 Mean   :2.5e-05   Mean   :2.5e-05   Mean   :5.01e-05   Mean   :2.5e-05  
 3rd Qu.:0.0e+00   3rd Qu.:0.0e+00   3rd Qu.:0.00e+00   3rd Qu.:0.0e+00  
 Max.   :1.0e+00   Max.   :1.0e+00   Max.   :1.00e+00   Max.   :1.0e+00  
                                                                         
   Montenegro         Morocco               N/A         
 Min.   :0.0e+00   Min.   :0.0000000   Min.   :0.00000  
 1st Qu.:0.0e+00   1st Qu.:0.0000000   1st Qu.:0.00000  
 Median :0.0e+00   Median :0.0000000   Median :0.00000  
 Mean   :2.5e-05   Mean   :0.0004756   Mean   :0.01407  
 3rd Qu.:0.0e+00   3rd Qu.:0.0000000   3rd Qu.:0.00000  
 Max.   :1.0e+00   Max.   :1.0000000   Max.   :1.00000  
                                                        
    Namibia             Nepal          Netherlands      
 Min.   :0.00e+00   Min.   :0.0e+00   Min.   :0.000000  
 1st Qu.:0.00e+00   1st Qu.:0.0e+00   1st Qu.:0.000000  
 Median :0.00e+00   Median :0.0e+00   Median :0.000000  
 Mean   :5.01e-05   Mean   :2.5e-05   Mean   :0.009687  
 3rd Qu.:0.00e+00   3rd Qu.:0.0e+00   3rd Qu.:0.000000  
 Max.   :1.00e+00   Max.   :1.0e+00   Max.   :1.000000  
                                                        
 Netherlands Antilles  New Zealand         Nicaragua      
 Min.   :0.0e+00      Min.   :0.000000   Min.   :0.0e+00  
 1st Qu.:0.0e+00      1st Qu.:0.000000   1st Qu.:0.0e+00  
 Median :0.0e+00      Median :0.000000   Median :0.0e+00  
 Mean   :2.5e-05      Mean   :0.003104   Mean   :2.5e-05  
 3rd Qu.:0.0e+00      3rd Qu.:0.000000   3rd Qu.:0.0e+00  
 Max.   :1.0e+00      Max.   :1.000000   Max.   :1.0e+00  
                                                          
     Niger            Nigeria           North Korea     
 Min.   :0.0e+00   Min.   :0.0000000   Min.   :0.0e+00  
 1st Qu.:0.0e+00   1st Qu.:0.0000000   1st Qu.:0.0e+00  
 Median :0.0e+00   Median :0.0000000   Median :0.0e+00  
 Mean   :2.5e-05   Mean   :0.0001001   Mean   :2.5e-05  
 3rd Qu.:0.0e+00   3rd Qu.:0.0000000   3rd Qu.:0.0e+00  
 Max.   :1.0e+00   Max.   :1.0000000   Max.   :1.0e+00  
                                                        
     Norway            Pakistan           Palestine        
 Min.   :0.000000   Min.   :0.0000000   Min.   :0.0000000  
 1st Qu.:0.000000   1st Qu.:0.0000000   1st Qu.:0.0000000  
 Median :0.000000   Median :0.0000000   Median :0.0000000  
 Mean   :0.005081   Mean   :0.0001252   Mean   :0.0002503  
 3rd Qu.:0.000000   3rd Qu.:0.0000000   3rd Qu.:0.0000000  
 Max.   :1.000000   Max.   :1.0000000   Max.   :1.0000000  
                                                           
     Panama          Papua New Guinea      Paraguay      
 Min.   :0.0000000   Min.   :0.00e+00   Min.   :0.0e+00  
 1st Qu.:0.0000000   1st Qu.:0.00e+00   1st Qu.:0.0e+00  
 Median :0.0000000   Median :0.00e+00   Median :0.0e+00  
 Mean   :0.0001752   Mean   :5.01e-05   Mean   :2.5e-05  
 3rd Qu.:0.0000000   3rd Qu.:0.00e+00   3rd Qu.:0.0e+00  
 Max.   :1.0000000   Max.   :1.00e+00   Max.   :1.0e+00  
                                                         
      Peru            Philippines          Poland        
 Min.   :0.0000000   Min.   :0.00000   Min.   :0.000000  
 1st Qu.:0.0000000   1st Qu.:0.00000   1st Qu.:0.000000  
 Median :0.0000000   Median :0.00000   Median :0.000000  
 Mean   :0.0006007   Mean   :0.00423   Mean   :0.009036  
 3rd Qu.:0.0000000   3rd Qu.:0.00000   3rd Qu.:0.000000  
 Max.   :1.0000000   Max.   :1.00000   Max.   :1.000000  
                                                         
    Portugal         Puerto Rico            Qatar         
 Min.   :0.000000   Min.   :0.0000000   Min.   :0.00e+00  
 1st Qu.:0.000000   1st Qu.:0.0000000   1st Qu.:0.00e+00  
 Median :0.000000   Median :0.0000000   Median :0.00e+00  
 Mean   :0.001927   Mean   :0.0002002   Mean   :5.01e-05  
 3rd Qu.:0.000000   3rd Qu.:0.0000000   3rd Qu.:0.00e+00  
 Max.   :1.000000   Max.   :1.0000000   Max.   :1.00e+00  
                                                          
 Republic of Macedonia    Romania             Russia        
 Min.   :0.0000000     Min.   :0.000000   Min.   :0.000000  
 1st Qu.:0.0000000     1st Qu.:0.000000   1st Qu.:0.000000  
 Median :0.0000000     Median :0.000000   Median :0.000000  
 Mean   :0.0001001     Mean   :0.002478   Mean   :0.004956  
 3rd Qu.:0.0000000     3rd Qu.:0.000000   3rd Qu.:0.000000  
 Max.   :1.0000000     Max.   :1.000000   Max.   :1.000000  
                                                            
  Saudi Arabia         Senegal              Serbia         
 Min.   :0.00e+00   Min.   :0.0000000   Min.   :0.0000000  
 1st Qu.:0.00e+00   1st Qu.:0.0000000   1st Qu.:0.0000000  
 Median :0.00e+00   Median :0.0000000   Median :0.0000000  
 Mean   :5.01e-05   Mean   :0.0003504   Mean   :0.0001001  
 3rd Qu.:0.00e+00   3rd Qu.:0.0000000   3rd Qu.:0.0000000  
 Max.   :1.00e+00   Max.   :1.0000000   Max.   :1.0000000  
                                                           
 Serbia and Montenegro   Singapore            Slovakia        
 Min.   :0.0e+00       Min.   :0.0000000   Min.   :0.0000000  
 1st Qu.:0.0e+00       1st Qu.:0.0000000   1st Qu.:0.0000000  
 Median :0.0e+00       Median :0.0000000   Median :0.0000000  
 Mean   :2.5e-05       Mean   :0.0005256   Mean   :0.0004005  
 3rd Qu.:0.0e+00       3rd Qu.:0.0000000   3rd Qu.:0.0000000  
 Max.   :1.0e+00       Max.   :1.0000000   Max.   :1.0000000  
                                                              
    Slovenia         Solomon Islands    South Africa     
 Min.   :0.0000000   Min.   :0.0e+00   Min.   :0.000000  
 1st Qu.:0.0000000   1st Qu.:0.0e+00   1st Qu.:0.000000  
 Median :0.0000000   Median :0.0e+00   Median :0.000000  
 Mean   :0.0005757   Mean   :2.5e-05   Mean   :0.003229  
 3rd Qu.:0.0000000   3rd Qu.:0.0e+00   3rd Qu.:0.000000  
 Max.   :1.0000000   Max.   :1.0e+00   Max.   :1.000000  
                                                         
  South Korea        Soviet Union         Spain             Sweden        
 Min.   :0.000000   Min.   :0.00000   Min.   :0.00000   Min.   :0.000000  
 1st Qu.:0.000000   1st Qu.:0.00000   1st Qu.:0.00000   1st Qu.:0.000000  
 Median :0.000000   Median :0.00000   Median :0.00000   Median :0.000000  
 Mean   :0.004606   Mean   :0.01079   Mean   :0.01785   Mean   :0.009962  
 3rd Qu.:0.000000   3rd Qu.:0.00000   3rd Qu.:0.00000   3rd Qu.:0.000000  
 Max.   :1.000000   Max.   :1.00000   Max.   :1.00000   Max.   :1.000000  
                                                                          
  Switzerland           Syria             Taiwan        
 Min.   :0.000000   Min.   :0.0e+00   Min.   :0.000000  
 1st Qu.:0.000000   1st Qu.:0.0e+00   1st Qu.:0.000000  
 Median :0.000000   Median :0.0e+00   Median :0.000000  
 Mean   :0.006082   Mean   :2.5e-05   Mean   :0.002128  
 3rd Qu.:0.000000   3rd Qu.:0.0e+00   3rd Qu.:0.000000  
 Max.   :1.000000   Max.   :1.0e+00   Max.   :1.000000  
                                                        
   Tajikistan          Tanzania           Thailand       
 Min.   :0.00e+00   Min.   :0.00e+00   Min.   :0.000000  
 1st Qu.:0.00e+00   1st Qu.:0.00e+00   1st Qu.:0.000000  
 Median :0.00e+00   Median :0.00e+00   Median :0.000000  
 Mean   :7.51e-05   Mean   :5.01e-05   Mean   :0.001126  
 3rd Qu.:0.00e+00   3rd Qu.:0.00e+00   3rd Qu.:0.000000  
 Max.   :1.00e+00   Max.   :1.00e+00   Max.   :1.000000  
                                                         
 The Democratic Republic Of Congo Trinidad and Tobago    Tunisia         
 Min.   :0.0e+00                  Min.   :0.00e+00    Min.   :0.0000000  
 1st Qu.:0.0e+00                  1st Qu.:0.00e+00    1st Qu.:0.0000000  
 Median :0.0e+00                  Median :0.00e+00    Median :0.0000000  
 Mean   :2.5e-05                  Mean   :5.01e-05    Mean   :0.0003504  
 3rd Qu.:0.0e+00                  3rd Qu.:0.00e+00    3rd Qu.:0.0000000  
 Max.   :1.0e+00                  Max.   :1.00e+00    Max.   :1.0000000  
                                                                         
     Turkey          Turkmenistan     Turks And Caicos Islands
 Min.   :0.000000   Min.   :0.0e+00   Min.   :0.0e+00         
 1st Qu.:0.000000   1st Qu.:0.0e+00   1st Qu.:0.0e+00         
 Median :0.000000   Median :0.0e+00   Median :0.0e+00         
 Mean   :0.002228   Mean   :2.5e-05   Mean   :2.5e-05         
 3rd Qu.:0.000000   3rd Qu.:0.0e+00   3rd Qu.:0.0e+00         
 Max.   :1.000000   Max.   :1.0e+00   Max.   :1.0e+00         
                                                              
       UK             Ukraine          United Arab Emirates
 Min.   :0.00000   Min.   :0.0000000   Min.   :0.0000000   
 1st Qu.:0.00000   1st Qu.:0.0000000   1st Qu.:0.0000000   
 Median :0.00000   Median :0.0000000   Median :0.0000000   
 Mean   :0.09078   Mean   :0.0004255   Mean   :0.0005507   
 3rd Qu.:0.00000   3rd Qu.:0.0000000   3rd Qu.:0.0000000   
 Max.   :1.00000   Max.   :1.0000000   Max.   :1.0000000   
                                                           
    Uruguay               USA           Uzbekistan       
 Min.   :0.0000000   Min.   :0.0000   Min.   :0.0000000  
 1st Qu.:0.0000000   1st Qu.:0.0000   1st Qu.:0.0000000  
 Median :0.0000000   Median :1.0000   Median :0.0000000  
 Mean   :0.0002253   Mean   :0.6264   Mean   :0.0001001  
 3rd Qu.:0.0000000   3rd Qu.:1.0000   3rd Qu.:0.0000000  
 Max.   :1.0000000   Max.   :1.0000   Max.   :1.0000000  
                                                         
    Vanuatu          Venezuela            Vietnam         
 Min.   :0.0e+00   Min.   :0.0000000   Min.   :0.0000000  
 1st Qu.:0.0e+00   1st Qu.:0.0000000   1st Qu.:0.0000000  
 Median :0.0e+00   Median :0.0000000   Median :0.0000000  
 Mean   :2.5e-05   Mean   :0.0002753   Mean   :0.0001252  
 3rd Qu.:0.0e+00   3rd Qu.:0.0000000   3rd Qu.:0.0000000  
 Max.   :1.0e+00   Max.   :1.0000000   Max.   :1.0000000  
                                                          
  West Germany       Yugoslavia           Zaire         
 Min.   :0.00000   Min.   :0.000000   Min.   :0.00e+00  
 1st Qu.:0.00000   1st Qu.:0.000000   1st Qu.:0.00e+00  
 Median :0.00000   Median :0.000000   Median :0.00e+00  
 Mean   :0.01209   Mean   :0.004881   Mean   :5.01e-05  
 3rd Qu.:0.00000   3rd Qu.:0.000000   3rd Qu.:0.00e+00  
 Max.   :1.00000   Max.   :1.000000   Max.   :1.00e+00  
                                                        
    Zimbabwe        
 Min.   :0.0000000  
 1st Qu.:0.0000000  
 Median :0.0000000  
 Mean   :0.0001001  
 3rd Qu.:0.0000000  
 Max.   :1.0000000  
                    
# cols = c('Genre', 'Action', 'Adult', 'Adventure', 'Animation', 'Biography', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Family', 'Fantasy', 'Film-Noir', 'Game-Show', 'History', 'Horror', 'Music', 'Musical', 'Mystery', 'N/A', 'News', 'Reality-TV', 'Romance', 'Sci-Fi', 'Short', 'Sport', 'Talk-Show', 'Thriller', 'War', 'Western')

# Remove column from dataframe
df$Country = NULL

# cols = setdiff(cols, 'Genre')
colCount = colSums(df[, 80:ncol(df)])
topTenIds = order(colCount,decreasing=TRUE)[1:70] + 1
topTenCols = names(df[, 80:ncol(df)][topTenIds])

# The top 30 most common countries are
print(topTenCols)
 [1] "Uzbekistan"                       "Ukraine"                         
 [3] "Gabon"                            "Chad"                            
 [5] "Ghana"                            "Jamaica"                         
 [7] "Jordan"                           "Indonesia"                       
 [9] "Sweden"                           "Namibia"                         
[11] "Yugoslavia"                       "Spain"                           
[13] "Switzerland"                      "Hungary"                         
[15] "Netherlands Antilles"             "Dominican Republic"              
[17] "Portugal"                         "Micronesia"                      
[19] "Benin"                            "Bulgaria"                        
[21] "Syria"                            "France"                          
[23] "Pakistan"                         "Saudi Arabia"                    
[25] "Isle Of Man"                      "Zaire"                           
[27] "Soviet Union"                     "Italy"                           
[29] "Poland"                           "Colombia"                        
[31] "Guatemala"                        "South Korea"                     
[33] "Nicaragua"                        "Burkina Faso"                    
[35] "Iceland"                          "Russia"                          
[37] "Denmark"                          "Czechoslovakia"                  
[39] "Turkmenistan"                     "Tajikistan"                      
[41] "Puerto Rico"                      "Iraq"                            
[43] "Malaysia"                         "China"                           
[45] "The Democratic Republic Of Congo" "Cyprus"                          
[47] "Iran"                             "Ecuador"                         
[49] "India"                            "Estonia"                         
[51] "Cuba"                             "Philippines"                     
[53] "Solomon Islands"                  "Uruguay"                         
[55] "Fiji"                             "Slovakia"                        
[57] "Mali"                             "N/A"                             
[59] "United Arab Emirates"             "Costa Rica"                      
[61] "Slovenia"                         "Ethiopia"                        
[63] "Serbia"                           "Turkey"                          
[65] "Cambodia"                         "Vietnam"                         
[67] "Brazil"                           "Kenya"                           
[69] "Panama"                           "Germany"                         
cols = c('Title', 'Runtime', 'Gross', 'Domestic_Gross')
cols = c(cols, topTenCols)

df9 = df[cols]
df9.long = melt(df9, id.vars=c('Title', 'Runtime', 'Gross', 'Domestic_Gross'), variable.name='country')
df9.long = df9.long[apply(df9.long['value'],1,function(z) !any(z==0)),]

print(summary(df9.long))
    Title              Runtime           Gross          
 Length:11296       Min.   :  1.00   Min.   :0.000e+00  
 Class :character   1st Qu.: 87.00   1st Qu.:5.760e+06  
 Mode  :character   Median : 96.00   Median :2.610e+07  
                    Mean   : 96.96   Mean   :7.038e+07  
                    3rd Qu.:109.00   3rd Qu.:9.151e+07  
                    Max.   :873.00   Max.   :1.215e+09  
                    NA's   :420      NA's   :10216      
 Domestic_Gross         country         value  
 Min.   :        0   France :2287   Min.   :1  
 1st Qu.:   859244   Germany:1460   1st Qu.:1  
 Median : 10015778   Italy  :1236   Median :1  
 Mean   : 28474360   N/A    : 986   Mean   :1  
 3rd Qu.: 38420602   India  : 725   3rd Qu.:1  
 Max.   :408992272   Spain  : 713   Max.   :1  
 NA's   :10216       (Other):3889              
print(head(df9.long))
           Title Runtime Gross Domestic_Gross    country value
3188   Aprel May      93    NA             NA Uzbekistan     1
3356  Cinedictum      40    NA             NA Uzbekistan     1
8067   Luna Papa     107    NA             NA Uzbekistan     1
8834    Bo Ba Bu      85    NA             NA Uzbekistan     1
41923      Divan      90    NA             NA    Ukraine     1
42383      Mamay      80    NA             NA    Ukraine     1
ggplot(df9.long, aes(as.factor(country), Runtime)) +
  geom_boxplot() +
  coord_flip() +
  scale_x_discrete("country")
Warning: Removed 420 rows containing non-finite values (stat_boxplot).

ggplot(df9.long, aes(as.factor(country), Gross)) +
  geom_boxplot() +
  coord_flip() +
  scale_x_discrete("country")
Warning: Removed 10216 rows containing non-finite values (stat_boxplot).

ggplot(df9.long, aes(as.factor(country), Domestic_Gross)) +
  geom_boxplot() +
  coord_flip() +
  scale_x_discrete("country")
Warning: Removed 10216 rows containing non-finite values (stat_boxplot).

Q: Expected insight #1.

A: First we plot runtime by country. While there is different distribution and sample size by country, notice that the median runtime by country is actually really similar. I would expect this because I am not aware of differences in runtime trends by country.

Q: Expected insight #2.

A:

Plotting the Gross Revenue by country, one expected insight is that lower income countries tend to have on average lower distribution of Gross Revenue.

10. Unexpected insight

Come up with one new insight (backed up by data and graphs) that is unexpected at first glance and do your best to motivate it. Same instructions apply as the previous task.

# TODO: Find and illustrate one unexpected insight
df$made_to_dvd = as.numeric(!is.na(df$DVD))

ggplot(df, aes(imdbRating, made_to_dvd)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")
Warning: Removed 1134 rows containing non-finite values (stat_summary).

Q: Unexpected insight.

A: One thing that was unexpected to me was I would have expected that there would be a positive correlation between ratings and whether the move was made to a DVD (better movies would more likely be made into a DVD). The relationship I found was somewhat of an “inversed-U” shape. At very low ratings, the proportion of DVD’s made is very low. Suprisingly, highly rated ratings also have lower made to DVD ratings.

---
title: "Project 1: Explore and Prepare Data"
subtitle: "CSE6242 - Data and Visual Analytics - Fall 2017\n\nDue: Sunday, October 15, 2017 at 11:59 PM UTC-12:00 on T-Square"
author: Vincent La (Georgia Tech ID - vla6)
date: October 12, 2017
output: html_notebook
---

_Note: This project involves getting data ready for analysis and doing some preliminary investigations. Project 2 will involve modeling and predictions on the same dataset, and will be released at a later date. Both projects will have equal weightage towards your grade. You may reuse some of the preprocessing/analysis steps from Project 1 in Project 2._

# Data

In this project, you will explore a dataset that contains information about movies, including ratings, budget, gross revenue and other attributes. It was prepared by Dr. Guy Lebanon, and here is his description of the dataset:

> The file [`movies_merged`](https://s3.amazonaws.com/content.udacity-data.com/courses/gt-cs6242/project/movies_merged) contains a dataframe with the same name that has 40K rows and 39 columns. Each row represents a movie title and each column represents a descriptor such as `Title`, `Actors`, and `Budget`. I collected the data by querying IMDb’s API (see [www.omdbapi.com](http://www.omdbapi.com/)) and joining it with a separate dataset of movie budgets and gross earnings (unknown to you). The join key was the movie title. This data is available for personal use, but IMDb’s terms of service do not allow it to be used for commercial purposes or for creating a competing repository.

# Objective

Your goal is to investigate the relationship between the movie descriptors and the box office success of movies, as represented by the variable `Gross`. This task is extremely important as it can help a studio decide which titles to fund for production, how much to bid on produced movies, when to release a title, how much to invest in marketing and PR, etc. This information is most useful before a title is released, but it is still very valuable after the movie is already released to the public (for example it can affect additional marketing spend or how much a studio should negotiate with on-demand streaming companies for “second window” streaming rights).

# Instructions

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. Open this file in RStudio to get started.

When you execute code within the notebook, the results appear beneath the code. Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Cmd+Shift+Enter*.

```{r}
# Project 1: http://cse6242.gatech.edu/fall-2017/pr1/
# To compile R Markdown in terminal run: Rscript -e "rmarkdown::render('pr1.Rmd', clean=TRUE)"
x = 1:10
print(x^2)
```

Plots appear inline too:
```{r}
plot(x, x^2, 'o')
```

Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Cmd+Option+I*. Enter some R code and run it.

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Cmd+Shift+K* to preview the HTML file).

Please complete all the tasks below by implementing code chunks that have a `TODO` comment in them, running all code chunks so that output and plots are displayed, and typing in answers to each question (**Q:** ...) next to/below the corresponding answer prompt (**A:**). Feel free to add code chunks/show additional output to support any of the answers.

When you are done, you will need to submit the final R markdown file (as **pr1.Rmd**) with all code chunks implemented and executed, and all text responses written in. You also need to submit a PDF export of the markdown file (as **pr1.pdf**), which should show your code, output, plots and written responses--this will be your project report. Compress these two files into a single .zip archive and upload it on T-Square.

# Setup

## Load data

Make sure you've downloaded the [`movies_merged`](https://s3.amazonaws.com/content.udacity-data.com/courses/gt-cs6242/project/movies_merged) file and it is in the current working directory. Now load it into memory:

```{r}
load('movies_merged')
cat("Dataset has", dim(movies_merged)[1], "rows and", dim(movies_merged)[2], "columns", end="\n", file="")
```

This creates an object of the same name (`movies_merged`). For convenience, you can copy it to `df` and start using it:

```{r}
df = movies_merged
cat("Column names:", end="\n", file="")
colnames(df)
```

## Load R packages

Load any R packages that you will need to use. You can come back to this chunk, edit it and re-run to load any additional packages later.

```{r}
library(ggplot2)
library(GGally)

# Note that I'm using Anaconda R, and I was having trouble installing qdapTools. I'm not sure
# if this is the same on other machines but I did have to install R XML separately
# https://anaconda.org/r/r-xml
# conda install -c r r-xml. Only after could I install qdapTools.
library(qdapTools)
```

If you are loading any non-standard packages (ones that have not been discussed in class or explicitly allowed for this project), please mention them below. Include any special instructions if they cannot be installed using the regular `install.packages('<pkg name>')` command.

**Non-standard packages used**: None

# Tasks

Each task below is worth **10** points, and is meant to be performed sequentially, i.e. do step 2 after you have processed the data as described in step 1. Total points: **100**

Complete each task by implementing code chunks as described by `TODO` comments, and by responding to questions ("**Q**:") with written answers ("**A**:"). If you are unable to find a meaningful or strong relationship in any of the cases when requested, explain why not by referring to appropriate plots/statistics.

It is okay to handle missing values below by omission, but please omit as little as possible. It is worthwhile to invest in reusable and clear code as you may need to use it or modify it in project 2.

## 1. Remove non-movie rows

The variable `Type` captures whether the row is a movie, a TV series, or a game. Remove all rows from `df` that do not correspond to movies.

```{r}
# TODO: Remove all rows from df that do not correspond to movies
df2 <- df[df$Type == "movie",]
original_dim = dim(df)
new_dim = dim(df2)

df = df2
# Differences in rows
print(original_dim[1] - new_dim[1])
```

**Q**: How many rows are left after removal? _Enter your response below._

**A**:
The number of rows left after removal are `r new_dim[1]`.

## 2. Process `Runtime` column

The variable `Runtime` represents the length of the title as a string. Write R code to convert it to a numeric value (in minutes) and replace `df$Runtime` with the new numeric column.

```{r}
extract_runtime = function(r){
    times = unlist(r)
    minutes = 0
    for (i in 1:length(times) - 1){
        if (times[i + 1] == 'h'){
            minutes = minutes + as.numeric(times[i]) * 60
        } else if (times[i + 1] == 'min'){
            minutes = minutes + as.numeric(times[i])
        }
    }
    if (minutes == 0){
        return(NA)
    } else{
        return(minutes)
    }
}

y=strsplit(df$Runtime,' ')
new_runtimes = unlist(lapply(y, extract_runtime))
df$Runtime = new_runtimes

cols = c('Runtime', 'Year', 'Budget')
summary(df[cols])
```

Now investigate the distribution of `Runtime` values and how it changes over years (variable `Year`, which you can bucket into decades) and in relation to the budget (variable `Budget`). Include any plots that illustrate.

```{r}
# TODO: Investigate the distribution of Runtime values and how it varies by Year and Budget
ggplot(df, aes(x=Runtime)) +
  geom_histogram() +
  ggtitle('Histogram of Runtime (minutes)')

ggplot(df, aes(Year, Runtime)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")

ggplot(df, aes(Budget, Runtime)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")
```

_Feel free to insert additional code chunks as necessary._

**Q**: Comment on the distribution as well as relationships. Are there any patterns or trends that you can observe?

**A**:

Looking in the aggregate, it seems like most runtimes are under 2 hours. There is a clear relationship between run time and year, looking a bit like a sigmoid function. This makes sense as very early years, it was probably technologically prohibitive to create long movies. As technology improved, runtimes increased, but there's only so much time you can expect to hold an audience captive. Interestingly enough, during the 1990's there seems to be a downward trend in runtimes until around mid 2000's where it picked up again.

## 3. Encode `Genre` column

The column `Genre` represents a list of genres associated with the movie in a string format. Write code to parse each text string into a binary vector with 1s representing the presence of a genre and 0s the absence, and add it to the dataframe as additional columns. Then remove the original `Genre` column.

For example, if there are a total of 3 genres: Drama, Comedy, and Action, a movie that is both Action and Comedy should be represented by a binary vector <0, 1, 1>. Note that you need to first compile a dictionary of all possible genres and then figure out which movie has which genres (you can use the R `tm` package to create the dictionary).

```{r}
# TODO: Replace Genre with a collection of binary columns
require(tm)

# All unique genres in the dataframe
print(unique(unlist(strsplit(df$Genre, ', '))))

# Example of how to one-hot encode: https://stackoverflow.com/questions/39778387/r-dataframe-one-hot-encoding-of-column-containing-multiple-terms

df = cbind(df, mtabulate(strsplit(df$Genre, ", ")))

cols = c('Genre', 'Action', 'Adult', 'Adventure', 'Animation', 'Biography', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Family', 'Fantasy', 'Film-Noir', 'Game-Show', 'History', 'Horror', 'Music', 'Musical', 'Mystery', 'N/A', 'News', 'Reality-TV', 'Romance', 'Sci-Fi', 'Short', 'Sport', 'Talk-Show', 'Thriller', 'War', 'Western')

# Remove column from dataframe
df$Genre = NULL
```

Plot the relative proportions of movies having the top 10 most common genres.

```{r}
# TODO: Select movies from top 10 most common genres and plot their relative proportions

# See https://stackoverflow.com/questions/20679702/r-find-column-with-the-largest-column-sum as reference
cols = setdiff(cols, 'Genre')
colCount = colSums(df[cols])
topTenIds = order(colCount,decreasing=TRUE)[1:10] + 1
topTenCols = names(df[cols][topTenIds])

# The top 10 most common genres are
print(topTenCols)

# Probably need to do some manual "stacking" of dataframes by each of the top 10 most common genres.
# Note that I was somewhat confused on what "relative proportions of movies" meant. I found this helpful
# https://piazza.com/class/j6gt7ycx6nk145?cid=396
# Suppose you have ten ice creams.  Let's say that four of them are Chocolate ice cream.  What is the relative proportion of Chocolate ice cream ?   --> 4 / 10
cols3 = c('Title', 'Runtime', topTenCols)
df3 = df[cols3]
print(summary(df3))
print(head(df3))

library(reshape2)
df3.long = melt(df3, id.vars=c('Title', 'Runtime'))

# Removing rows where value is 0 to answer the question
# This is because when value is 0, that means movie is not that genre.
df3.long = df3.long[apply(df3.long['value'],1,function(z) !any(z==0)),]
print(summary(df3.long))
print(head(df3.long))

# Finally plot relative proportions
# https://sebastiansauer.github.io/percentage_plot_ggplot2_V2/
ggplot(df3.long, aes(x=variable)) +
  geom_bar(aes(y = (..count..)/sum(..count..))) +
  ylab('Relative Proportion')
```

Examine how the distribution of `Runtime` changes across genres for the top 10 most common genres.

```{r}
# TODO: Plot Runtime distribution for top 10 most common genres
# ggplot(df3.long, aes(x=Runtime)) +
#   geom_density(aes(color=variable, group=variable)) +
#   ggtitle('Distribution of Runtimes by Movie Genre')

ggplot(df3.long, aes(as.factor(variable), Runtime)) +
  geom_boxplot() +
  coord_flip() +
  scale_x_discrete("Genre")
```

**Q**: Describe the interesting relationship(s) you observe. Are there any expected or unexpected trends that are evident?

**A**:
One thing that immediately stood out to me was that in looking at the extreme outliers (Runtimes greater than 300 minutes for example or 5 hours), most of these very long movies tend to be Drama, War, and Documentaries, which fits anecdotally with my priors of what movies would be extremely long.

Animation films tend to be the shortest, which is expected as there are probably many short animation films. I was actually surprised to see that most animation films are very very short.

One that that was unexpected to me was that with the exception of Animation and Family films, most of the median runtimes for all the rest of the genres are very similar.

## 4. Eliminate mismatched rows

The dataframe was put together by merging two different sources of data and it is possible that the merging process was inaccurate in some cases (the merge was done based on movie title, but there are cases of different movies with the same title). There are 3 columns that contain date information: `Year` (numeric year), `Date` (numeric year), and `Released` (string representation of the release date).

Find and remove all rows where you suspect a merge error occurred based on a mismatch between these variables. To make sure subsequent analysis and modeling work well, avoid removing more than 10% of the rows that have a `Gross` value present.

_Note: Do not remove the rows with `Gross == NA` at this point, just use this a guideline._

```{r}
# TODO: Remove rows with Year/Date/Released mismatch
library(lubridate)  # Used to extract year from Released

cols4 = c('Year', 'Date', 'Released', 'Gross')
df4 = df[cols4]
df4$released_year = year(df4$Released)
df4$not_matched = (df4$Year != df4$released_year) & (df4$Year != df4$Date) & (df4$Date != df4$released_year)

# Number of rows with non-null Gross Value
print(nrow(df[which(!is.na(df$Gross)), ]))

# Printing rows in the original data set
print(nrow(df))

df = df[which(df4$not_matched == FALSE | is.na(df4$not_matched)), ]
print(nrow(df))
```

**Q**: What is your precise removal logic, and how many rows remain in the resulting dataset?

**A**:
Out of the 40,000 original rows, I removed rows where `Year != released_year` and `Year != Date` and `Date != released_year` This resulted in `r nrow(df4[which(df4$not_matched == TRUE & !is.na(df4$Gross)), ])` rows to be removed where there was a non-null `Gross` value. Since there were originally `r nrow(df[which(!is.na(df$Gross)), ])` rows with a non-null `Gross` value, we have indeed removed less than 10 percent of the rows with non-null `Gross` values.

There are now `r nrow(df)` rows in the resulting dataset.

## 5. Explore `Gross` revenue

For the commercial success of a movie, production houses want to maximize Gross revenue. Investigate if Gross revenue is related to Budget, Runtime or Genre in any way.

_Note: To get a meaningful relationship, you may have to partition the movies into subsets such as short vs. long duration, or by genre, etc._

```{r}
# TODO: Investigate if Gross Revenue is related to Budget, Runtime or Genre
cols = c('Title', 'Gross', 'Runtime', 'Budget', 'Released', 'Action', 'Adult', 'Adventure', 'Animation', 'Biography', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Family', 'Fantasy', 'Film-Noir', 'Game-Show', 'History', 'Horror', 'Music', 'Musical', 'Mystery', 'N/A', 'News', 'Reality-TV', 'Romance', 'Sci-Fi', 'Short', 'Sport', 'Talk-Show', 'Thriller', 'War', 'Western')
df5 = df[cols]

print(summary(df5))
print(head(df5))

ggplot(df, aes(Runtime, Gross)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")

ggplot(df, aes(Budget, Gross)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")

# Next melt the df to get genre as one column (like in question 3) and plot grouped by genre
df5.long = melt(df5, id.vars=c('Title', 'Gross', 'Runtime', 'Budget', 'Released'), variable.name='genre')

# Removing rows where value is 0 to answer the question
# This is because when value is 0, that means movie is not that genre.
df5.long = df5.long[apply(df5.long['value'], 1, function(z) !any(z==0)),]

library(plyr)
df5.long.runtime <- ddply(df5.long, c('Runtime', 'genre'), function(x) mean(x$Gross))
names(df5.long.runtime)[names(df5.long.runtime) == 'V1'] <- 'Gross'
df5.long.runtime = df5.long.runtime[!df5.long.runtime$Runtime > 180,]  # Removing movies greater than 3 hours

df5.long.budget <- ddply(df5.long, c('Budget', 'genre'), function(x) mean(x$Gross))
names(df5.long.budget)[names(df5.long.budget) == 'V1'] <- 'Gross'

ggplot(df5.long.runtime, aes(Runtime, Gross)) +
  geom_point(aes(color=genre, group=genre))

ggplot(df5.long.budget, aes(Budget, Gross)) +
  geom_point(aes(color=genre, group=genre))
```

**Q**: Did you find any observable relationships or combinations of Budget/Runtime/Genre that result in high Gross revenue? If you divided the movies into different subsets, you may get different answers for them - point out interesting ones.

**A**:

Yes, it seems that in the aggregate, Gross revenue is positive correlated with Runtime, but only to a certain extent. After about 3 hours or so, the relationship is much murkier. Gross Revenue seems generally positively correlated with Budget.

```{r}
# TODO: Investigate if Gross Revenue is related to Release Month
print(summary(df5.long))
print(head(df5.long))
df$released_month = month(df$Released)
ggplot(df, aes(released_month, Gross)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")
```

There is definitely a relationship of Gross Revenue and release month. Summer month movies and holiday movies gross significantly more revenue than movies released in other months.

## 6. Process `Awards` column

The variable `Awards` describes nominations and awards in text format. Convert it to 2 numeric columns, the first capturing the number of wins, and the second capturing nominations. Replace the `Awards` column with these new columns, and then study the relationship of `Gross` revenue with respect to them.

_Note: The format of the `Awards` column is not standard; you may have to use regular expressions to find the relevant values. Try your best to process them, and you may leave the ones that don't have enough information as NAs or set them to 0s._

```{r}
# TODO: Convert Awards to 2 numeric columns: wins and nominations
library(stringi)
cols = c('Title', 'Awards', 'Gross')
df6 = df[cols]
print(unique(df6$Awards))

print(summary(df6))
print(head(df6))

awards = stri_extract_all(df6$Awards, regex="\\d+")
award_labels = stri_extract_all(df6$Awards, regex="win|won|nomin")
print(head(awards))
print(head(award_labels))

wins_list = c()
nominations_list = c()

for (i in 1:length(awards)){
    wins = 0
    nominations = 0
    for(j in 1:length(awards[[i]])){
        if(!is.na(award_labels[[i]][j]) & (award_labels[[i]][j] == 'wins' | award_labels[[i]][j] == 'win' | award_labels[[i]][j] == 'won')){
            wins = wins + as.numeric(awards[[i]][j])
        } else if (!is.na(award_labels[[i]][j]) & award_labels[[i]][j] == 'nomin'){
            nominations = nominations + as.numeric(awards[[i]][j])
        }
    }
    wins_list = c(wins_list, wins)
    nominations_list = c(nominations_list, nominations)
}

df$wins = wins_list
df$nominations = nominations_list

print(head(df[c('Awards', 'wins', 'nominations')], 20))
```

**Q**: How did you construct your conversion mechanism? How many rows had valid/non-zero wins or nominations?

**A**:
To construct the conversion mechanism, I first used `stri_extract_all` to extract all numerical substrings. I then extracted all instances of "win, won, or nomin". I then assigned the numerical to however it corresponded to the position of "win, won, or nomin".


```{r}
# TODO: Plot Gross revenue against wins and nominations
ggplot(df, aes(wins, Gross)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")

ggplot(df, aes(nominations, Gross)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")
```

**Q**: How does the gross revenue vary by number of awards won and nominations received?

**A**:
Surprisingly, there does not seem to be much of a relationship between gross revenue and awards won.


## 7. Movie ratings from IMDb and Rotten Tomatoes

There are several variables that describe ratings, including IMDb ratings (`imdbRating` represents average user ratings and `imdbVotes` represents the number of user ratings), and multiple Rotten Tomatoes ratings (represented by several variables pre-fixed by `tomato`). Read up on such ratings on the web (for example [rottentomatoes.com/about](https://www.rottentomatoes.com/about) and [ www.imdb.com/help/show_leaf?votestopfaq](http:// www.imdb.com/help/show_leaf?votestopfaq)).

Investigate the pairwise relationships between these different descriptors using graphs.

```{r}
library(GGally)

# TODO: Illustrate how ratings from IMDb and Rotten Tomatoes are related
cols = c('imdbRating', 'imdbVotes', 'tomatoMeter', 'tomatoImage', 'tomatoRating', 'tomatoReviews',
         'tomatoFresh', 'tomatoRotten', 'tomatoConsensus', 'tomatoUserMeter', 'tomatoUserRating',
         'tomatoUserReviews')
df7 = df[cols]

print(summary(df7))
print(head(df7))

ggpairs(df7, columns=c('imdbRating', 'tomatoMeter', 'tomatoRating'), aes())
```

**Q**: Comment on the similarities and differences between the user ratings of IMDb and the critics ratings of Rotten Tomatoes.

**A**:
First off, it's interesting to note that we have significantly more data of IMDB ratings than Rotten tomatoes. In our data, we have about 1100 movies with missing IMDB ratings. However, we have close to 30,000 movies with missing Rotten Tomatoes ratings.

In the plot above we plot the `ggpairs` of `imdbRating`, `tomatoMeter`, and `tomatoRating`. Notice that the correlation between `tomatoMeter` and `tomatoRating` is very high, at 0.94. This makes sense as it's from the same source. The correlation between `tomatoMeter` and `imdbRating` is a bit lower, at around 0.746 vs 0.794 which is the correlation between `tomatoRating` and `imdbRating`.

Looking at the summary of the variables as well, it looks like `imdbRating` and `tomatoRating` are rated on similar scales. `imdbRating` looks like it's rated on a scale of 1 to 10, while `tomatoRating` looks like it's rated on a scale of 0 to 9.8. However, `tomatoMeter` looks like it's scaled from 0 to 100.

## 8. Ratings and awards

These ratings typically reflect the general appeal of the movie to the public or gather opinions from a larger body of critics. Whereas awards are given by professional societies that may evaluate a movie on specific attributes, such as artistic performance, screenplay, sound design, etc.

Study the relationship between ratings and awards using graphs (awards here refers to wins and/or nominations).

```{r}
# TODO: Show how ratings and awards are related
ggplot(df, aes(imdbRating, wins)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")

ggplot(df, aes(imdbRating, nominations)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")

ggplot(df, aes(tomatoRating, wins)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")

ggplot(df, aes(tomatoRating, nominations)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")


mod1 = lm(wins ~ imdbRating, data = df)
summary(mod1)

mod1 = lm(nominations ~ imdbRating, data = df)
summary(mod1)

mod1 = lm(wins ~ tomatoRating, data = df)
summary(mod1)

mod1 = lm(nominations ~ tomatoRating, data = df)
summary(mod1)
```

**Q**: How good are these ratings in terms of predicting the success of a movie in winning awards or nominations? Is there a high correlation between two variables?

**A**:
One extremely interesting trend from the graph above is that for both `imdbRatings` and `tomatoRatings` generally there is a positive correlation between ratings and both wins and nominations. But very interestingly enough, the relationship is actually not monotonic. In the extremely upper regions of ratings, the number of wins and nominations actually decrease!

I fit a linear regression between wins and nominations against ratings field. Notice that in each regression the coefficient is significant, which means that there is a significant positive relationship between rating and nominations and wins. However, the R^2 is relatively low, so the correlation is relatively low.

## 9. Expected insights

Come up with two new insights (backed up by data and graphs) that is expected. Here “new” means insights that are not an immediate consequence of one of the above tasks. You may use any of the columns already explored above or a different one in the dataset, such as `Title`, `Actors`, etc.

```{r}
# TODO: Find and illustrate two expected insights
df = cbind(df, mtabulate(strsplit(df$Country, ", ")))
print(summary(df))

# cols = c('Genre', 'Action', 'Adult', 'Adventure', 'Animation', 'Biography', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Family', 'Fantasy', 'Film-Noir', 'Game-Show', 'History', 'Horror', 'Music', 'Musical', 'Mystery', 'N/A', 'News', 'Reality-TV', 'Romance', 'Sci-Fi', 'Short', 'Sport', 'Talk-Show', 'Thriller', 'War', 'Western')

# Remove column from dataframe
df$Country = NULL

# cols = setdiff(cols, 'Genre')
colCount = colSums(df[, 80:ncol(df)])
topTenIds = order(colCount,decreasing=TRUE)[1:70] + 1
topTenCols = names(df[, 80:ncol(df)][topTenIds])

# The top 30 most common countries are
print(topTenCols)

cols = c('Title', 'Runtime', 'Gross', 'Domestic_Gross')
cols = c(cols, topTenCols)

df9 = df[cols]
df9.long = melt(df9, id.vars=c('Title', 'Runtime', 'Gross', 'Domestic_Gross'), variable.name='country')
df9.long = df9.long[apply(df9.long['value'],1,function(z) !any(z==0)),]

print(summary(df9.long))
print(head(df9.long))

ggplot(df9.long, aes(as.factor(country), Runtime)) +
  geom_boxplot() +
  coord_flip() +
  scale_x_discrete("country")

ggplot(df9.long, aes(as.factor(country), Gross)) +
  geom_boxplot() +
  coord_flip() +
  scale_x_discrete("country")

ggplot(df9.long, aes(as.factor(country), Domestic_Gross)) +
  geom_boxplot() +
  coord_flip() +
  scale_x_discrete("country")

```

**Q**: Expected insight #1.

**A**:
First we plot runtime by country. While there is different distribution and sample size by country, notice that the median runtime by country is actually really similar. I would expect this because I am not aware of differences in runtime trends by country.

**Q**: Expected insight #2.

**A**:

Plotting the Gross Revenue by country, one expected insight is that lower income countries tend to have on average lower distribution of Gross Revenue.

## 10. Unexpected insight

Come up with one new insight (backed up by data and graphs) that is unexpected at first glance and do your best to motivate it. Same instructions apply as the previous task.

```{r}
# TODO: Find and illustrate one unexpected insight
df$made_to_dvd = as.numeric(!is.na(df$DVD))

ggplot(df, aes(imdbRating, made_to_dvd)) +
  stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")
```

**Q**: Unexpected insight.

**A**:
One thing that was unexpected to me was I would have expected that there would be a positive correlation between ratings and whether the move was made to a DVD (better movies would more likely be made into a DVD). The relationship I found was somewhat of an "inversed-U" shape. At very low ratings, the proportion of DVD's made is very low. Suprisingly, highly rated ratings also have lower made to DVD ratings.
